Inner Product Regularized Nonnegative Self Representation for Image Classification and Clustering

IEEE Access

卷:5

Chao Bi¹  Wei Zhou²  Yanjiao Shi³  Yuanlong Cao⁴  Guoliang Luo⁴  Yugen Yi⁴ 

[1] College of Computer Science and Information Technology, Northeast Normal University, Changchun, China;

[2] College of Information Science and Engineering, Northeastern University, Shenyang, China;

[3] School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China;

[4] School of Software, Jiangxi Normal University, Nanchang, China;

关键词: Unsupervised feature selection;    self-representation;    inner product regularization;    Image classification;    image clustering;   

DOI  :  10.1109/ACCESS.2017.2724763

来源: DOAJ

【 摘 要 】

Feature selection, which aims to select the most informative feature subset, has been playing a critical role in dimension reduction. In this paper, a novel unsupervised feature selection algorithm called the inner product regularized nonnegative self-representation (IRNSR) is designed for image classification and clustering. In the IRNSR algorithm, first, each feature in high-dimensional data is represented by a linear combination of other features. Then, the inner product regularized loss function is introduced into the objective function with the aim of reducing the correlation and redundancy among the selected features. More importantly, a simple yet efficient iterative update optimization algorithm is accordingly designed to solve the objective function. The convergence behavior of the proposed optimization algorithm is also analyzed. Comparative experiments on six image databases indicate that the proposed IRNSR algorithm is effective and efficient.

【 授权许可】

Unknown