Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural gradient works efficiently in learning
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
A Local Region-based Approach to Gender Classi.cation From Face Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Semantic manifold learning for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
Multimodal oriented discriminant analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-supervised nonlinear dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
An Experimental Study on Automatic Face Gender Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Multiplicative updates for non-negative projections
Neurocomputing
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-Supervised Learning
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Facial expression recognition using kernel canonical correlation analysis (KCCA)
IEEE Transactions on Neural Networks
A comparative study of dimensionality reduction techniques to enhance trace clustering performances
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Fisher discriminant analysis gives the unsatisfactory results if points in the same class have within-class multimodality and fails to produce the non-negativity of projection vectors. In this paper, we focus on the newly formulated within and between-class scatters based supervised locality preserving dimensionality reduction problem and propose an effective dimensionality reduction algorithm, namely, Multiplicative Updates based non-negative Discriminative Learning (MUNDL), which optimally seeks to obtain two non-negative embedding transformations with high preservation and discrimination powers for two data sets in different classes such that nearby sample pairs in the original space compact in the learned embedding space, under which the projections of the original data in different classes can be appropriately separated from each other. We also show that MUNDL can be easily extended to nonlinear dimensionality reduction scenarios by employing the standard kernel trick. We verify the feasibility and effectiveness of MUNDL by conducting extensive data visualization and classification experiments. Numerical results on some benchmark UCI and real-world datasets show the MUNDL method tends to capture the intrinsic local and multimodal structure characteristics of the given data and outperforms some established dimensionality reduction methods, while being much more efficient.