Feedforward neural networks for principal components extraction
Computational Statistics & Data Analysis
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Eigenregions for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustered Blockwise PCA for Representing Visual Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA-based web page watermarking
Pattern Recognition
Multimodal biometric system using rank-level fusion approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Block-wise 2D kernel PCA/LDA for face recognition
Information Processing Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
A novel incremental principal component analysis and its application for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Principal Component Analysis (PCA) is one of the well-known linear dimensionality reduction techniques in the literature. Large computational requirements of PCA and its insensitivity to 'local' variations in patterns motivated to propose partitional based PCA approaches. It is also observed that these partitioning methods are incapable of extracting 'global' information in patterns thus showing lower dimensionality reduction. To alleviate the problems faced by PCA and the partitioning based PCA methods, SubXPCA was proposed to extract principal components with global and local information. In this paper, we prove analytically that (i) SubXPCA shows its computational efficiency up to a factor of k (k=2) as compared to PCA and competitive to an existing partitioning based PCA method (SubPCA), (ii) SubXPCA shows much lower classification time as compared to SubPCA method, (iii) SubXPCA and SubPCA outperform PCA by a factor up to k (k=2) in terms of space complexity. The effectiveness of SubXPCA is demonstrated upon a UCI data set and ORL face data.