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A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition
Joint Authors
Hao, Ningbo
Yang, Jie
Liao, Haibin
Dai, Wenhua
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-26
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Various methods for feature extraction and dimensionality reduction have been proposed in recent decades, including supervised and unsupervised methods and linear and nonlinear methods.
Despite the different motivations of these methods, we present in this paper a general formulation known as factor analysis to unify them within a common framework.
During factor analysis, an object can be seen as being comprised of content and style factors, and the objective of feature extraction and dimensionality reduction is to obtain the content factor without style factor.
There are two vital steps in factor analysis framework; one is the design of factor separating objective function, including the design of partition and weight matrix, and the other is the design of space mapping function.
In this paper, classical Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP) algorithms are improved based on factor analysis framework, and LDA based on factor analysis (FA-LDA) and LPP based on factor analysis (FA-LPP) are proposed.
Experimental results show the superiority of our proposed approach in classification performance compared to classical LDA and LPP algorithms.
American Psychological Association (APA)
Hao, Ningbo& Yang, Jie& Liao, Haibin& Dai, Wenhua. 2016. A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1112855
Modern Language Association (MLA)
Hao, Ningbo…[et al.]. A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition. Mathematical Problems in Engineering No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1112855
American Medical Association (AMA)
Hao, Ningbo& Yang, Jie& Liao, Haibin& Dai, Wenhua. A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1112855
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112855