Orthogonal neighborhood preserving discriminant analysis for face recognition
Pattern Recognition
Kernel-based learning for biomedical relation extraction
Journal of the American Society for Information Science and Technology
Rotating Fault Diagnosis Based on Wavelet Kernel Principal Component
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A multiexpert collaborative biometric system for people identification
Journal of Visual Languages and Computing
Incremental learning of bidirectional principal components for face recognition
Pattern Recognition
A New Incremental PCA Algorithm With Application to Visual Learning and Recognition
Neural Processing Letters
Context cells: towards lifelong learning in activity recognition systems
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast incremental kernel principal component analysis for online feature extraction
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Kernel discriminant transformation for image set-based face recognition
Pattern Recognition
Query answering on trajectory cuboids using prime numbers encodings
Proceedings of the 15th Symposium on International Database Engineering & Applications
Efficient and effective query answering for trajectory cuboids
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Incremental face recognition for large-scale social network services
Pattern Recognition
Interactive particle visualization with advanced shading models using lazy evaluation
EG PGV'07 Proceedings of the 7th Eurographics conference on Parallel Graphics and Visualization
Warehousing and querying trajectory data streams with error estimation
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
Next challenges for adaptive learning systems
ACM SIGKDD Explorations Newsletter
Incremental face recognition: hybrid approach using short-term memory and long-term memory
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Computational and space complexity analysis of SubXPCA
Pattern Recognition
Efficient eigen-updating for spectral graph clustering
Neurocomputing
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Principal component analysis (PCA) has been proven to be an efficient method in pattern recognition and image analysis. Recently, PCA has been extensively employed for face-recognition algorithms, such as eigenface and fisherface. The encouraging results have been reported and discussed in the literature. Many PCA-based face-recognition systems have also been developed in the last decade. However, existing PCA-based face-recognition systems are hard to scale up because of the computational cost and memory-requirement burden. To overcome this limitation, an incremental approach is usually adopted. Incremental PCA (IPCA) methods have been studied for many years in the machine-learning community. The major limitation of existing IPCA methods is that there is no guarantee on the approximation error. In view of this limitation, this paper proposes a new IPCA method based on the idea of a singular value decomposition (SVD) updating algorithm, namely an SVD updating-based IPCA (SVDU-IPCA) algorithm. In the proposed SVDU-IPCA algorithm, we have mathematically proved that the approximation error is bounded. A complexity analysis on the proposed method is also presented. Another characteristic of the proposed SVDU-IPCA algorithm is that it can be easily extended to a kernel version. The proposed method has been evaluated using available public databases, namely FERET, AR, and Yale B, and applied to existing face-recognition algorithms. Experimental results show that the difference of the average recognition accuracy between the proposed incremental method and the batch-mode method is less than 1%. This implies that the proposed SVDU-IPCA method gives a close approximation to the batch-mode PCA method