Applied multivariate statistical analysis
Applied multivariate statistical analysis
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Computing Clusters of Correlation Connected objects
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Computational Statistics & Data Analysis
Pattern Recognition Letters
Information Sciences: an International Journal
An adaptively weighted sub-pattern locality preserving projection for face recognition
Journal of Network and Computer Applications
A structure-preserved local matching approach for face recognition
Pattern Recognition Letters
Learning-based super resolution using kernel partial least squares
Image and Vision Computing
Weighted principal component extraction with genetic algorithms
Applied Soft Computing
Face recognition based on rearranged modular 2DPCA
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Computational and space complexity analysis of SubXPCA
Pattern Recognition
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In this paper we propose a general feature partitioning framework to PCA computation and raise issues of cross-sub-pattern correlation, feature ordering dependence, selection of sub-pattern size, overlap of sub-patterns and selection of principal components. These issues are critical to the design and performance of feature partitioning approaches to PCA computation. We show several open issues and present a novel algorithm, SubXPCA which proposes a solution to the cross-sub-pattern correlation issue in the feature partitioning framework. SubXPCA is shown to be a general technique since we derive PCA and SubPCA as special cases of SubXPCA. We show SubXPCA has theoretically better time complexity as compared to PCA. Comprehensive experimentation on UCI repository data and face data sets (ORL, CMU, Yale) confirms the superiority of SubXPCA with better classification accuracy. SubXPCA not only has better time performance but is also superior in its summarization of variance as compared to SubPCA. SubXPCA is shown to be robust in its performance with respect to feature ordering and overlapped sub-patterns.