Kernel-preserving Embedding Based Subspace Learning

Subspace learning is an important research subject in the field of feature extraction.It maps the original data into a low-dimensional subspace through a linear or nonlinear transformation, and preserves the geometric structure and useful information of the original data as much as possible in this...

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Bibliographic Details
Published inJi suan ji ke xue Vol. 48; no. 6; p. 79
Main Authors He, Wen-Qi, Liu, Bao-Long, Sun, Zhao-Chuan, Wang, Lei, Li, Dan-Ping
Format Journal Article
LanguageChinese
Published Chongqing Guojia Kexue Jishu Bu 01.01.2021
Subjects
Algorithms
Classification
Data mining
Embedding
Feature extraction
Kernel functions
Machine learning
Optimization
Similarity
Subspaces
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Summary:Subspace learning is an important research subject in the field of feature extraction.It maps the original data into a low-dimensional subspace through a linear or nonlinear transformation, and preserves the geometric structure and useful information of the original data as much as possible in this subspace.The performance of subspace learning mainly depends on the design of similarity measure and the graph construction for feature embedding.Aiming at the two issues, a novel kernel-preserving embedding based subspace learning(KESL) method is proposed, which can adaptively learn the similarity information from data and construct the kernel-preserving graph.First, to tackle the problem that the traditional dimension reduction methods cannot preserve the inner structure of high-dimensional nonlinear data, our algorithm introduces the kernel function and minimizes the reconstruction error of samples, which is beneficial for mining the data structural relationship for classification.Then, aiming at the limitation
ISSN:1002-137X