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papersTODAY 04:00 UTC

arXiv Paper Proposes Rotation-Based Subspace Tracking for Kernel PCA on Streaming Data

A new arXiv preprint introduces a rotation-based method for tracking subspaces when applying kernel principal component analysis to data streams. The approach targets robustness against data drift, where the underlying distribution of incoming data shifts over time. It aims to keep dimensionality reduction and feature extraction reliable in settings where datasets are not static.

arXiv Data drift Dimensionality reduction Kernel principal component analysis Streaming data Subspace tracking

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arXiv cs.LGRotation-Based Subspace Tracking for Robust Kernel PCA on Streaming Data ↗TODAY 04:00 UTC