Local Discriminant Wavelet Packet Coordinates for Face Recognition
The Journal of Machine Learning Research
Semantic analysis of real-world images using support vector machine
Expert Systems with Applications: An International Journal
Localized null space representation for dynamic updating and downdating in image and video databases
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Face recognition from still images to video sequences: a local-feature-based framework
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Classification of 3-D objects and faces employing view-based clusters
Computers and Electrical Engineering
Directional two-dimensional principal component analysis for face recognition
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Fast protein superfamily classification using principal component null space analysis
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Subspace-based clustering and retrieval of 3-D objects
Computers and Electrical Engineering
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We present a new classification algorithm, principal component space analysis (PCNSA), which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices. PCNSA first obtains a principal components subspace (PCA space) for the entire data. In this PCA space, it finds for each class "i", an Mi-dimensional subspace along which the class' intraclass variance is the smallest. We call this subspace an approximate space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class' mean in the class' ANS is a minimum. We derive upper bounds on classification error probability of PCNSA and use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA). We propose a practical modification of PCNSA called progressive-PCNSA that also detects "new" (untrained classes). Finally, we provide an experimental comparison of PCNSA and progressive PCNSA with SLDA and PCA and also with other classification algorithms-linear SVMs, kernel PCA, kernel discriminant analysis, and kernel SLDA, for object recognition and face recognition under large pose/expression variation. We also show applications of PCNSA to two classification problems in video-an action retrieval problem and abnormal activity detection.