Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition

被引：3

作者：

Liu, Chang ^{[1
,2
]}

Yan, Tao ^{[1
,2
]}

Zhao, WeiDong ^{[1
,2
]}

Liu, YongHong ^{[1
,2
]}

Li, Dan ^{[1
,2
]}

Lin, Feng ^{[3
]}

Zhou, JiLiu ^{[3
]}

机构：

[1] Chengdu Univ, Coll Informat Sci & Technol, Chengdu 610106, Peoples R China

[2] Inst Higher Educ Sichuan Prov, Key Lab Pattern Recognit & Intelligent Informat P, Chengdu 610106, Peoples R China

[3] Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2014年 / 2014卷

基金：

中国国家自然科学基金;

关键词：

DISCRIMINANT-ANALYSIS; FACE REPRESENTATION; 2-DIMENSIONAL PCA; DIMENSIONALITY; PROJECTIONS;

D O I：

10.1155/2014/819758

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA) based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA), incremental principal component analysis (IPCA), and multilinear principal component analysis (MPCA) algorithms. At the same time, ITPCA also has lower time and space complexity.

引用

页数：10

共 50 条

[1] Handwritten digit recognition by a mixture of local principal component analysis
Zhang, BL
Fu, MY
Yan, H
[J]. NEURAL PROCESSING LETTERS, 1998, 8 (03) : 241 - 251
[2] Handwritten Digit Recognition by a Mixture of Local Principal Component Analysis
Bailing Zhang
Minyue Fu
Hong Yan
[J]. Neural Processing Letters, 1998, 8 : 241 - 252
[3] Incremental tensor principal component analysis for image recognition
[J]. 1600, Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland (710):
[4] Handwritten Digit Recognition Based on Principal Component Analysis and Support Vector Machines
Li, Rui
Zhang, Shiqing
[J]. ADVANCES IN COMPUTER SCIENCE, ENVIRONMENT, ECOINFORMATICS, AND EDUCATION, PT I, 2011, 214 : 595 - 599
[5] Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices
Peng, Chao-Chung
Huang, Chao-Yang
Chen, Yi-Ho
[J]. ELECTRONICS, 2023, 12 (18)
[6] Evaluating SPAN Incremental Learning for Handwritten Digit Recognition
Mohemmed, Ammar
Lu, Guoyu
Kasabov, Nikola
[J]. NEURAL INFORMATION PROCESSING, ICONIP 2012, PT III, 2012, 7665 : 670 - 677
[7] Offline handwritten numeral recognition based on principal component analysis
Wan Junli
Huang Yuehua
Zhang Guohua
Wan Cheng
[J]. ICEMI 2007: PROCEEDINGS OF 2007 8TH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOL I, 2007, : 298 - 302
[8] Multilinear Principal Component Analysis of tensor objects for recognition
Lu, Haiping
Plataniotis, K. N.
Venetsanopoulos, A. N.
[J]. 18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS, 2006, : 776 - +
[9] Handwritten digit character recognition by model reconstruction based on independent component analysis
Rui, Ting
Shen, Chunlin
Ding, Jian
Jiang, Nan
[J]. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2005, 17 (03): : 455 - 460
[10] Incremental Class Learning approach and its application to Handwritten Digit Recognition
Mandziuk, J
Shastri, L
[J]. ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3, 1998, : 203 - 206

← 1 2 3 4 5 →