A Novel Feature Extraction Algorithm Based on Joint Learning

Author

Jeng-Shyang Pan ; Lijun Yan ; Zongguang Fang

Author_Institution

Shenzhen Grad. Sch., Harbin Inst. of Technol., Shenzhen, China

fYear

2013

fDate

10-12 Dec. 2013

Firstpage

31

Lastpage

34

Abstract

In this paper, a novel feature extraction algorithm, called Joint Discriminant Sparse Neighborhood Preserving Embedding (JDSNPE), based on Discriminant Sparse Neighborhood Preserving Embedding (DSNPE) and joint learning is proposed. JDSNPE aims to get the row sparsity of the transformation matrix while preserving discriminant sparse neighborhood. Experimental results on Yale database demonstrate the effectiveness of the proposed algorithm compared to Sparse Neighborhood Preserving Embedding and DSNPE.

Keywords

face recognition; feature extraction; image classification; learning (artificial intelligence); matrix algebra; visual databases; JDSNPE algorithm; Yale database; feature extraction algorithm; joint discriminant sparse neighborhood preserving embedding algorithm; joint learning; transformation matrix row sparsity; Face; Face recognition; Feature extraction; Joints; Principal component analysis; Signal processing algorithms; Sparse matrices; Joint learning; discriminant sparse neighborhood;

fLanguage

English

Publisher

ieee

Conference_Titel

Robot, Vision and Signal Processing (RVSP), 2013 Second International Conference on

Conference_Location

Kitakyushu

Print_ISBN

978-1-4799-3183-5

Type

conf

DOI

10.1109/RVSP.2013.15

Filename

6824655