An online incremental orthogonal component analysis method for dimensionality reduction.
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.
National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, PR China. Electronic address: tao144@gmail.com.
Tao
T
Zhu
6211 Sudikoff Lab, Dartmouth College Hanover, NH 03755, United States. Electronic address: ye@cs.dartmouth.edu.
Ye
Y
Xu
National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, PR China. Electronic address: frshen@nju.edu.cn.
Furao
F
Shen
National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, PR China. Electronic address: jxzhao@nju.edu.cn.
Jinxi
J
Zhao
D000465:Algorithms; D000069550:Machine Learning; D016571:Neural Networks, Computer
Automatic target dimension estimation; Dimensionality reduction; Incremental learning; Online learning; Orthogonal component