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Nonlinear Dimensionality Reduction Using a Temporal Coherence Principle by YaPing Huang; JiaLi Zhao; YunHui Liu; SiWei Luo; Qi Zou; Mei Tian is a Computer Science article available to read on EtoBox.

What is Nonlinear Dimensionality Reduction Using a Temporal Coherence Principle about?

Temporal coherence principle is an attractive biologically inspired learning rule to extract slowly varying features from quickly varying input data. In this paper we develop a new Nonlinear Neighborhood Preserving (NNP) technique, by utilizing the temporal coherence principle to find an optimal low dimensional representation from the original high dimensional data. NNP is based on a nonlinear expansion of the original input data, such as polynomials of a given degree. It can be solved by the eigenvalue problem without using gradient descent and is guaranteed to find the global optimum. NNP can be viewed as a nonlinear dimensionality reduction framework which takes into consideration both time series and data sets without an obvious temporal structure. According to different situations, we introduce three algorithms of NNP, named NNP-1, NNP-2, and NNP-3. The objective function of NNP-1 is equal to Slow Feature Analysis (SFA), and it works well for time series such as image sequences. NNP-2 artificially constructs time series consisting of neighboring points for data sets without a clear temporal structure such as image data. NNP-3 is proposed for classification tasks, which can min

Who reads Nonlinear Dimensionality Reduction Using a Temporal Coherence Principle?

Author

YaPing Huang; JiaLi Zhao; YunHui Liu; SiWei Luo; Qi Zou; Mei Tian

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0020-0255)

Published

2011

Language

EN

Field

Computer Science (Physical Sciences)