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An Evolutionary Orthogonal Component Analysis Method for Incremental Dimensionality Reduction
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DOI：10.1109/TNNLS.2020.3027852.png)
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Abstract
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In order to quickly discover the low-dimensional representation of high-dimensional noisy data in online environments, we transform the linear dimensionality reduction problem into the problem of learning the bases of linear feature subspaces. Based on that, we propose a fast and robust dimensionality reduction framework for incremental subspace learning named evolutionary orthogonal component analysis (EOCA). By setting adaptive thresholds to automatically determine the target dimensionality, the proposed method extracts the orthogonal subspace bases of data incrementally to realize dimensionality reduction and avoids complex computations. Besides, EOCA can merge two learned subspaces that are represented by their orthonormal bases to a new one to eliminate the outlier effects, and the new subspace is proved to be unique. Extensive experiments and analysis demonstrate that EOCA is fast and achieves competitive results, especially for noisy data.
Keywords:
Dimensionality reduction
Matrix decomposition
Learning systems
Feature extraction
Principal component analysis
Data mining
Estimation
Dimensionality reduction
incremental learning
orthogonal component (OC)
subspace learning
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