Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Information Sciences: an International Journal
A new and fast implementation for null space based linear discriminant analysis
Pattern Recognition
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
Nonlinear dimensionality reduction using a temporal coherence principle
Information Sciences: an International Journal
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
A new covariance estimate for Bayesian classifiers in biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Fast dimension reduction for document classification based on imprecise spectrum analysis
Information Sciences: an International Journal
Using the idea of the sparse representation to perform coarse-to-fine face recognition
Information Sciences: an International Journal
Automatic field data analyzer for closed-loop vehicle design
Information Sciences: an International Journal
Two-factor face authentication using matrix permutation transformation and a user password
Information Sciences: an International Journal
| Hi-index | 0.07 |
The small sample size problem is often encountered in pattern recognition. Several algorithms for null space based linear discriminant analysis (NLDA) have been developed to solve the problem. However, these algorithms for NLDA have high computational cost. In this paper, we simplify the recently proposed algorithm for NLDA in Chu and Thye (2010) [5] with the assumption that all the training data vectors are linearly independent and propose a new and fast algorithm for NLDA. Our main observation is that two steps of economic QR decomposition with column pivoting can be replaced by one step of economic QR decomposition without column pivoting if the related matrix is of full column rank. The main features of our algorithm for NLDA include: (i) our NLDA algorithm is carried out by only one step of economic QR decomposition and does not compute any singular value decomposition (SVD) when all the training data vectors are linearly independent; (ii) the main cost of our method is the cost of an economic QR decomposition of an mx(n-1) matrix, here m is the dimension of the training data and n is the number of samples. Our method is a fast one. Experimental studies on ORL, FERET and PIE face databases demonstrate the effectiveness of our new algorithm for NLDA.