Tag: dimensionality reduction

A new study in Financial Innovation finds that cross-sectional market state data improves short-horizon forecasts for the Dow Jones but ...

Modeling text embeddings as cyclostationary sequences improves BERT-based multilingual sentiment analysis accuracy by up to 28% across five diverse datasets.

A cluster-preserving feature selection framework cut intrusion detection features from 78 to 27 on the CICIDS2017 benchmark while Random Forest ...

Researchers in Stuttgart have introduced a machine-learning framework that identifies stable, passive port-Hamiltonian models directly from data, validated on systems ...

A hybrid framework combining quantum-annealing-based feature selection with PCA reduced carbon-emissions prediction errors by 4 to 7 percent compared with ...

A new Oxford study introduces OFTER, an online forecasting pipeline that combines ARIMA models, PCA-based dimensionality reduction, k-nearest neighbors and ...

A lightweight spatial attention module lets a 3D convolutional autoencoder compress atmospheric concentration fields with competitive accuracy, differentiability, and minimal ...

A machine learning framework called BioBombe maps the biological processes that cancers depend on for survival, revealing new vulnerabilities and ...

Researchers have unveiled PMFS-NRDLC, a unified framework that removes noisy labels and selects informative features simultaneously for partial multi-label learning, ...

Researchers used hierarchical agglomerative clustering to sort the Veterans Health Administration's eighteen regions into four interpretable profiles that align with ...