An online incremental orthogonal component analysis method for dimensionality reduction. (January 2017)

• Record Type: Journal Article
• Authors: Zhu, Tao; Xu, Ye; Shen, Furao; Zhao, Jinxi
• Is Part Of: Neural networks. Volume 85(2017)
• Page Start: 33
• Page End: 50
• Publication Date: 2017-01
• Subjects: Dimensionality reduction -- Orthogonal component -- Incremental learning -- Automatic target dimension estimation -- Online learning; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.10.001
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, we introduce a fast linear dimensionality reduction method named incremental orthogonal component analysis (IOCA). IOCA is designed to automatically extract desired orthogonal components (OCs) in an online environment. The OCs and the low-dimensional representations of original data are obtained with only one pass through the entire dataset. Without solving matrix eigenproblem or matrix inversion problem, IOCA learns incrementally from continuous data stream with low computational cost. By proposing an adaptive threshold policy, IOCA is able to automatically determine the dimension of feature subspace. Meanwhile, the quality of the learned OCs is guaranteed. The analysis and experiments demonstrate that IOCA is simple, but efficient and effective.