The impact of missing data on the validity of study estimates depends heavily on why the data are missing. Pregnant women from lower socioeconomic backgrounds tend to access antenatal care later in pregnancy and enroll later in research studies, which means they accumulate more missing data early in pregnancy. This is particularly problematic because socioeconomic status is itself an important determinant of drinking behavior during pregnancy. Women also modify their consumption after recognizing they are pregnant, and the timing of that recognition varies widely. Some stop or reduce drinking immediately upon learning of the pregnancy, while some heavy drinkers continue to binge in the first trimester or maintain heavy drinking throughout gestation. Standard approaches to handling missing values, such as last value carry forward or mean imputation, are poorly suited to this setting because drinking behavior during pregnancy varies by gestational timing and by day of the week, and because the pattern of missingness is itself linked to drinking recency.

A team of researchers led by Ayesha Sania of Columbia University Irving Medical Center and the New York State Psychiatric Institute, working with colleagues at NYU Grossman School of Medicine, Avera Health, the University of South Dakota, and Stellenbosch University in Cape Town, has now demonstrated a machine learning solution to this problem. Writing in Advances in Drug and Alcohol Research, the team applied the k-nearest neighbor algorithm, or k-NN, to impute missing daily alcohol consumption data from the Safe Passage Study, a prospective cohort investigation conducted by the Prenatal Alcohol and SIDS and Stillbirth Network. The Safe Passage Study enrolled 11,892 pregnant women between August 2007 and January 2015 from antenatal clinics in the Northern Plains of the United States and in Cape Town, South Africa, with 11,083 women included in the imputation analysis. Participants were eligible if they were pregnant with one or two fetuses, aged 16 years or older, at least six weeks gestational age at recruitment, and spoke English or Afrikaans. Women were followed throughout pregnancy and for one year postnatally, with ethical approval obtained from institutional review boards including Stellenbosch University, Sanford Health, the Indian Health Service, and participating Tribal Nations.

Alcohol exposure data in the Safe Passage Study were collected using a modified version of the Timeline Followback method, a validated self-report instrument in which participants report drinking on their last known drinking day and then for the 30 days prior. The instrument captured remarkable detail: types and number of drinks, container sizes, the amount of ice in each drink, how many people shared a drink, and the duration of drinking episodes. These data were converted into estimates of total alcohol consumed and the number of standard drinks on each reported drinking day. Yet the design of the method inherently generates a variable amount of missing data per participant. Women who never drank, or whose last drinking day preceded their last menstrual period, had complete records. Women who drank but quit within 30 days of a collection period had few or no gaps. But women who continued to drink and reported their most recent 30 days of intake left unrecorded intervals stretching back toward the start of pregnancy. Frequent drinkers consequently accumulated more missing days than infrequent drinkers, meaning that simple summation of reported drinks would systematically underestimate true consumption and bias any analysis built on that metric.

The k-NN algorithm addresses this challenge through pattern recognition rather than statistical modeling. As a non-parametric method, it makes no distributional assumptions about the underlying data, an advantage given that alcohol consumption during pregnancy is highly skewed, with most drinking concentrated in the first trimester before pregnancy recognition. The algorithm works by measuring the similarity between the drinking pattern of a participant with missing data and the patterns of other participants in the database. Similarity is quantified using cosine distance, which captures the shape of a drinking pattern regardless of its absolute magnitude. Once the k most similar participants are identified, the weighted average of their drinking values for the missing day serves as the best estimate, with nearer neighbors exerting greater influence than distant ones. The team also scaled imputed values to each individual’s consumption level, ensuring that a participant with a similar pattern but heavier overall intake was not matched to a lighter drinker’s absolute values.

Data preparation required careful engineering. The team converted the records so that each participant occupied a single row, with drinking values as separate variables for each day from 15 days before the last menstrual period through day 310 of pregnancy. Because drinking behavior varies by day of the week, participants were temporally aligned so that, for example, a participant whose pregnancy began on a Wednesday was compared with others whose corresponding days also fell on Wednesdays. Outlier drinking values were Winsorized, or capped, at three standard deviations to limit their influence on imputed values. Participants lacking any first-trimester data or missing more than 200 days were excluded, since their drinking patterns could not be reliably established. A crucial methodological insight shaped the reference pool: women with completely missing-free records were more likely to be nondrinkers or light drinkers, so using only them as neighbors would underestimate true drinking. Instead, the team included segments of non-missing data from all participants as reference material.

Validation proceeded by randomly deleting data for 5 to 15 consecutive days from the first-trimester records of participants with no missing data, then comparing imputed values against the actual deleted values. The first trimester was chosen because both the proportion of women drinking and the magnitude of their drinking are highest then, particularly before pregnancy recognition. The team examined root mean squared error across values of k from 1 to 10 and across reference segment lengths. They found that five nearest neighbors and reference segments of 55 days, roughly two months, provided imputed values with the least error. Shorter segments did not meaningfully increase the available reference pool, while the 55-day length preserved the algorithm’s ability to characterize specific drinking patterns. For nondrinking segments, a single neighbor produced the lowest error, but multiple neighbors improved prediction of drinking segments, making k equal to 5 a reasonable compromise for both.

The results were striking. Across 3.2 million person-days of observation, 0.36 million person-days, or 11.4 percent, were missing. About 45 percent of participants had alcohol data for every single day of pregnancy, while the remaining 55 percent had at least one day missing. Sixty-two percent of participants were drinkers, consuming at least one drink during pregnancy. After deleting data segments from complete first-trimester records, the algorithm produced no difference between actual and predicted values for 64 percent of deleted segments. For another 31 percent of segments, imputed values fell within plus or minus one drink per day of the actual values. The algorithm exactly predicted drinking status for 76 percent of drinking segments in the combined analysis. Accuracy varied by study site: nondrinking segments were predicted within one drink for 78.6 percent of deleted segments in South Africa and 67.6 percent in the Northern Plains, reflecting differences in the magnitude of drinking and the proportion of missing data between sites.

Imputation also shifted population-level estimates in informative ways. Following imputation, the mean number of drinks in the first trimester increased by an average of two drinks in South Africa, while the increase for the Northern Plains sites was just below one drink. The Northern Plains had fewer missing data points overall, with over half of participants having 30 or fewer missing days, and lower proportions of drinkers and drinking volumes. Most missing data at the South African site came from early trimesters, reflecting later enrollment there, whereas most missing data in the Northern Plains occurred in the third trimester. This distinction matters because early-pregnancy missing data are more likely to represent active drinking periods, given that many women reduce or stop drinking after learning of the pregnancy.

The study is, to the authors’ knowledge, the first to describe this method for imputing missing alcohol consumption data in a longitudinal study of pregnant women. Prior comparative work had found that multiple imputation and full information maximum likelihood yielded less biased estimates than complete case analysis or last observation carried forward, but even those methods underestimated actual drinking levels, and imputing irregularly spaced longitudinal data would require complex extensions of standard multiple imputation. The non-parametric nature of k-NN sidesteps the normality assumptions embedded in most standard software packages, making it well suited to skewed, high-volume daily data. The k-NN algorithm is increasingly used to impute missing data in high-volume research areas such as genetics and metabolomics, where it has often produced the smallest imputation error compared with mean and median imputation, Bayesian linear regression, and clustering approaches. The authors note that the algorithm cannot overcome bias introduced when participants misreport drinking days as nondrinking days, and that future studies should evaluate alternative distance measures and other machine learning approaches. The methodological details presented here hold relevance well beyond alcohol research, extending to substance use and nutrition studies that suffer from informative missing data in longitudinal designs.

Subject of Research: Machine learning imputation of missing prenatal alcohol exposure data

Article Title: K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data

Article References: Sania, A., Pini, N., Nelson, M. E., Myers, M. M., Shuffrey, L. C., Lucchini, M., Elliott, A. J., Odendaal, H. J., & Fifer, W. P. (2025). K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data. Advances in Drug and Alcohol Research, 4, Article 13449. https://doi.org/10.3389/adar.2024.13449

Image Credits: AI Generated

DOI: 10.3389/adar.2024.13449

Keywords: k-nearest neighbor, machine learning, data imputation, prenatal alcohol exposure, missing data, longitudinal studies, Safe Passage Study, Timeline Followback, epidemiology, alcohol research, K-nearest, neighbor

The core idea behind prototype-based clustering is elegant. Instead of comparing every data point with every other data point, which becomes computationally prohibitive at scale, the algorithm learns a small set of representative anchors, or prototypes, that capture the global semantic structure of the data. Each sample can then be described by its relationship to these prototypes, dramatically reducing the cost of grouping similar items together. In multi-view settings, prototypes also serve as a common language: if the prototypes learned from an image view and a text view can be aligned, the algorithm can fuse information across modalities even when individual samples lack one of the two descriptions. Prototype-based deep clustering methods have consequently attracted significant attention in recent years, precisely because of this capacity to summarize global structure efficiently.

Yet the authors of the new study identify two weaknesses that plague existing prototype-based multi-view methods. The first is an assumption of quality. Most approaches simply trust that the features produced by the front-end encoders, the neural networks that convert raw data into numerical representations, are clean and informative. When those features are noisy, which they often are when views are missing and the network has less information to work with, the prototypes generated from them inherit the noise, and the entire clustering pipeline degrades. The second weakness concerns what the researchers call the prototype-unaligned problem. Because different views describe the same underlying objects in different ways, the prototypes learned from each view rarely line up neatly. Methods that try to force alignment often go too far, emphasizing inter-view consistency so heavily that they wash away the complementary and diverse information that makes multi-view learning valuable in the first place.

RPCV addresses the first weakness at its source, inside the feature extraction stage. The authors design a transformer-based cross-view attention module, drawing on the attention architecture that Vaswani and colleagues introduced in 2017 and that now underpins most of modern artificial intelligence. Attention mechanisms work by letting one part of a network dynamically weigh the relevance of other parts; here, the module leverages contextual information from the views that are actually observed to guide the inference of latent representations for the views that are missing. In other words, when the algorithm encounters a sample whose text description is absent, it can consult the image view and, guided by learned attention patterns, infer what the missing representation should look like. This dynamic guidance serves a dual purpose: it suppresses feature noise before prototypes are ever computed, and it enhances the discriminability of the latent representations, making it easier for downstream stages to separate distinct clusters.

The second challenge, heterogeneity across views, is handled by what the authors call confidence-weighted interpolated relational consistency learning. Consistency learning is a strategy borrowed from contrastive learning, a family of techniques popularized by frameworks such as SimCLR and momentum contrast, in which a network is trained to treat related items as similar and unrelated items as dissimilar. In the multi-view setting, consistency learning encourages the representations of the same object, seen from different views, to agree with one another. RPCV refines this idea by weighting each consistency signal according to a confidence measure and interpolating between relational structures. Samples or views that the model trusts more contribute more strongly to the alignment, while uncertain signals are down-weighted. This mitigates the heterogeneity of multi-view data, acknowledging that some views are more reliable, or more informative, for some samples than others, rather than pretending all views are equally trustworthy at all times.

The heart of the method is its robust prototype learning module, which the authors describe as being trained through dual optimization. The innovation here is the incorporation of a joint entropy constraint during the prototype relation learning phase. Entropy, in information theory, quantifies uncertainty; a distribution that spreads probability evenly across many outcomes has high entropy, while one concentrated on a single outcome has low entropy. By constraining the joint entropy of prototype relations, RPCV strikes an explicit balance between two competing goals: maintaining cross-view semantic alignment, so that prototypes from different views refer to the same underlying concepts, and preserving inter-view differences, so that each view retains its distinctive perspective. Too much alignment and the method collapses into redundant copies of the same information; too little and the views fail to communicate. The entropy constraint provides a principled mathematical mechanism for navigating between these extremes, something earlier methods achieved only heuristically, if at all.

Finally, RPCV employs a prototype-based imputation strategy to recover the missing multi-view data directly. Imputation, the practice of filling in missing values, has a long history in statistics, but doing it in deep representation space is delicate. Rather than generating missing views from scratch with a generative model, RPCV uses the learned prototypes as anchors: a missing representation is reconstructed by referencing the prototype relations of the sample’s observed views. Because the prototypes have already been made robust through the entropy-constrained learning process, the imputed values inherit that robustness. The recovered views then feed back into the overall clustering objective, creating a system in which feature extraction, consistency learning, prototype alignment, and imputation reinforce one another rather than operating as isolated stages.

The authors report extensive experimental results demonstrating that RPCV achieves superior performance on clustering tasks compared to a variety of existing incomplete multi-view methods. The evaluation landscape for this field is crowded: prior approaches include graph completion techniques, adaptive graph learning, contrastive prediction frameworks such as Completer, adversarial methods, dual contrastive calibration, and prototype-based imputation schemes presented at major venues including IJCAI and the IEEE Conference on Computer Vision and Pattern Recognition. The fact that RPCV outperforms this diverse set of baselines suggests that its combined innovations, noise suppression at the feature stage, confidence-weighted consistency, and entropy-balanced prototype alignment, address genuine gaps rather than incremental refinements. The researchers have made their code publicly available on GitHub, allowing other teams to reproduce the results and build upon the framework, and the datasets used in the study are available from the corresponding author on reasonable request.

The significance of this work extends beyond benchmark tables. Incomplete data is not an edge case; it is the norm in domains ranging from medical imaging, where a patient may have a scan but no genetic profile, to multimedia retrieval, where videos may have captions in one language but not another, to sensor networks, where devices fail unpredictably. Methods that can gracefully recover missing information while respecting the distinct character of each available view are therefore of broad practical interest. The cross-view attention design is particularly notable because it treats missing views not as a defect to be patched after the fact but as a signal to be inferred from context, echoing the masked-modeling philosophy that has proven so successful in recent vision and language models.

There are, of course, the usual caveats that accompany any new machine learning method. The study appears in a peer-reviewed journal and reports comparisons against multiple baselines, but independent replication on new domains will be the true test of generality. Transformer-based attention adds computational overhead, and the practical cost of the dual optimization and entropy constraints at very large scale remains to be seen. Still, the conceptual contribution is clear: by refusing to choose between consistency and diversity, and by hardening the feature pipeline before prototypes are formed, RPCV offers a template for how prototype-based clustering can be made robust to the imperfections that real-world data inevitably contains. As multi-view data continues to proliferate across science, industry, and everyday applications, frameworks of this kind point toward clustering systems that are not only accurate when conditions are ideal, but dependable when they are not. The work was supported by the National Natural Science Foundation of China, the Shanghai Natural Science Foundation, and the Shanghai Oriental Talent Program-Youth Program.

Subject of Research: Incomplete multi-view clustering using cross-view attention and robust prototype alignment

Article Title: RPCV: robust prototype alignment via cross-view attention for incomplete multi-view clustering

Article References: Li, H., & Zhu, C. (2026). RPCV: robust prototype alignment via cross-view attention for incomplete multi-view clustering. Applied Intelligence, 56(15), Article 477. https://doi.org/10.1007/s10489-026-07508-3

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07508-3

Keywords: incomplete multi-view clustering, cross-view attention, prototype learning, transformer, contrastive learning, deep clustering, data imputation, entropy constraint, machine learning, Applied Intelligence, Shanghai Maritime University, unsupervised learning

A team of researchers at Guangdong University of Technology, led by Bo Liu of the School of Automation, has now tackled this practical weakness head-on. In a study published in Neural Computing and Applications, the authors introduce a framework called Explainability-Guided Partial Privileged Learning, abbreviated EPPL, which is designed to keep LUPI methods effective even when only a fraction of the privileged information is actually available. The work addresses a scenario that is far more common in industry and medicine than the idealized complete-privileged-information setting: some training samples come with the extra knowledge, and many do not, and nobody has the resources to fill in all the gaps.

The core challenge the researchers identified is how to reconstruct the missing privileged information without contaminating the learning process with noise. A natural strategy is similarity transfer: if two training examples look alike in their ordinary feature space, one might infer that their privileged descriptions are also similar, allowing the privileged representation of a fully documented example to stand in for a sparsely documented one. The trouble is that naive similarity calculations in high-dimensional spaces are notoriously fragile. Two samples may appear close together because of background texture, lighting artifacts, or other irrelevant signals, while being semantically far apart in terms of what actually distinguishes their classes. This semantic misalignment means that blind similarity transfer can inject misleading surrogate information into the training pipeline, degrading rather than improving the final classifier.

EPPL’s central innovation is to stop being blind. The framework integrates an Explainable AI mechanism, specifically LIME, the Local Interpretable Model-agnostic Explanations technique introduced by Marco Ribeiro and colleagues in 2016. LIME probes a classifier by perturbing its inputs and observing how predictions change, producing a local explanation that highlights which features actually drove a given decision. In EPPL, these LIME-derived masks act as filters that separate discriminative features, the ones that genuinely matter for classification, from background noise. When the system computes similarity between two training samples to transfer privileged information, it weights the comparison by these masks, so that the distance is dominated by features the classifier itself has identified as meaningful rather than by incidental similarities across the full high-dimensional representation.

On top of this explanation-guided similarity measure, the researchers designed a class-aware weighted similarity strategy with two jobs. First, it improves the accuracy of imputing missing privileged information, because samples of the same class tend to share relevant privileged structure that class-conscious matching can exploit. Second, and crucially, the same similarity machinery dynamically assigns confidence weights to the downstream SVM+ classifier. SVM+, Vapnik’s extension of the support vector machine to the LUPI setting, learns a correcting function over the privileged space alongside the main decision function. If the imputed privileged information for a given sample is unreliable, treating it with the same confidence as genuinely observed data would be a mistake. EPPL instead down-weights samples whose reconstructed privileged information rests on weak similarity evidence, letting the classifier lean harder on well-supported examples and more gently on uncertain ones.

The quantitative results reported in the study are striking, particularly in the hardest regime. When only 20 percent of the privileged information was available, a severe data-scarce scenario, EPPL outperformed state-of-the-art baseline methods by up to 3.12 percent in classification accuracy. Equally important is the framework’s stability. As the proportion of available privileged information dropped from 80 percent to 20 percent, EPPL’s performance degraded by only 9.32 percent, while other competitive models suffered drops exceeding 12 percent. In a field where the gap between a good model and a deployable model is often measured in fractions of a percentage point, both the accuracy advantage and the reduced degradation represent meaningful progress.

The experiments were conducted on four publicly available benchmark datasets spanning very different domains: the Handwritten Digital Images dataset from the UCI Machine Learning Repository, the Caltech-101 object recognition collection, the Animals with Attributes 2 dataset, and the Corel image dataset. Testing across handwritten digits, natural object photographs, animal categories, and generic imagery gives the results a breadth that single-dataset studies often lack, and the consistent advantage of EPPL across these benchmarks suggests that the benefit derives from the framework itself rather than from idiosyncrasies of any one data domain. All four datasets are freely accessible, which should make it straightforward for other groups to reproduce and extend the findings.

Beyond the headline numbers, the study carries a broader conceptual message about the relationship between explainability and learning efficiency. Explainable AI is usually framed as a tool for human trust: producing heatmaps, feature rankings, and textual justifications so that people can audit what a model is doing. EPPL repurposes those same explanations for a purely technical role, using them as a signal that improves the learning pipeline itself. The LIME masks do not exist to reassure a clinician or a regulator; they exist to decide which pixel or feature dimensions should carry weight when the algorithm judges whether two training examples are true semantic neighbors. This inversion, in which explanations become functional components rather than post-hoc reporting layers, hints at a productive research direction where interpretability methods pay for themselves in raw performance.

The practical implications reach into any domain where privileged information is expensive. Medical imaging is an obvious candidate: expert annotations, additional imaging modalities, and longitudinal follow-up data all function as privileged information that cannot be gathered for every case. Financial modeling is another, where analysts’ private assessments or delayed regulatory filings provide training-time knowledge that is unavailable in real time. In settings such as these, the robustness curve matters as much as the peak. A method that performs brilliantly with complete information but craters when coverage drops to a fifth of the dataset forces practitioners into painful trade-offs between data budgets and model quality. EPPL’s comparatively gentle degradation suggests that partial coverage can be an acceptable operating point rather than a failure mode.

The research was supported by the Natural Science Foundation of China under Grant 62076074 and the Natural Science Foundation of Guangdong Province under Grant 2023A1515012560. The source code is available from the corresponding author upon reasonable request, and the authors report no competing financial interests. As privileged learning matures from a theoretical curiosity into a practical toolkit, the question of incomplete information is the question that will decide its real-world reach, and this work offers a concrete, explanation-driven answer to it.

Subject of Research: Explainability-guided partial privileged learning using class-aware weighted similarity to handle incomplete privileged information in machine learning classifiers

Article Title: Explainability-guided partial privileged learning: a class-aware weighted similarity approach

Article References: Liu, B., Sun, Y., Xiao, Y., Zeng, W., Tang, Q., Jiang, X., Chen, J., & Yang, Z. (2026). Explainability-guided partial privileged learning: a class-aware weighted similarity approach. Neural Computing and Applications, 38(17), Article 734. https://doi.org/10.1007/s00521-026-12316-4

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12316-4

Keywords: privileged information, LUPI, explainable AI, LIME, SVM+, similarity transfer, machine learning, classification, partial privileged learning, Neural Computing and Applications, Guangdong University of Technology, data imputation