|Table of Contents|

Robust and Sparse BPCA with the Constraints of L1-norm and the Elastic Net(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年04期
Page:
102-109
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Robust and Sparse BPCA with the Constraints of L1-norm and the Elastic Net
Author(s):
Tang Ganyi1; Lu Guifu1; Wang Yong1; 2; Fan Lili1; Du Yangfan1
(1.School of Computer and Information,Anhui Polytechnic University,Wuhu 241000,China)
(2.State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China)
Keywords:
BPCA; L1-norm; elastic net; sparse modelling; subspace learning
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2022.04.014
Abstract:
Block principal component analysis(BPCA),which can utilize part correlation of image matrix sufficiently,is an important subspace learning approach. L1-norm based BPCA is an effective technique for robust learning in dimensionality reduction developed recently. We propose a novel robust and sparse BPCA method referred to as BPCAL1-S. The approach is more robust to outliers than the traditional L2-norm based PCA. To develop a model with sparsity,the elastic net constraint which combining ridge and lasso penalty,is integrated into the optimization procedure. We present a greedy algorithm to extract basic feature vectors one by one,and proposed theoretical analysis to guarantee the convergence of the iterative process. The proposed BPCAL1-S is applied to the analysis of image classification and image reconstruction,and the experimental results verify its effectiveness.

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Last Update: 2022-12-15