Neighborhood MinMax projections

Authors:
Feiping Nie;Shiming Xiang;Changshui Zhang
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

Citing 5
Cited 16

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized Foley-Sammon transform based on generalized fisher discriminant criterion and its application to face recognition

Pattern Recognition Letters
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Semi-supervised sub-manifold discriminant analysis

Pattern Recognition Letters
Extracting the optimal dimensionality for local tensor discriminant analysis

Pattern Recognition
Semi-supervised orthogonal discriminant analysis via label propagation

Pattern Recognition
Trace ratio criterion for feature selection

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning

MM '09 Proceedings of the 17th ACM international conference on Multimedia
Laplacian Discriminant Projection Based on Affinity Propagation

AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
A general kernelization framework for learning algorithms based on kernel PCA

Neurocomputing
Exemplar based Laplacian Discriminant Projection

Expert Systems with Applications: An International Journal
Analysis of Correlation Based Dimension Reduction Methods

International Journal of Applied Mathematics and Computer Science - Issues in Advanced Control and Diagnosis
Fast neighborhood component analysis

Neurocomputing
Incremental complete LDA for face recognition

Pattern Recognition
Exemplar based laplacian discriminant projection

ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Graph embedding based feature selection

Neurocomputing
Biview face recognition in the shape-texture domain

Pattern Recognition
Dimension estimation of image manifolds by minimal cover approximation

Neurocomputing
Low-rank representation based discriminative projection for robust feature extraction

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new algorithm, Neighborhood MinMax Projections (NMMP), is proposed for supervised dimensionality reduction in this paper. The algorithm aims at learning a linear transformation, and focuses only on the pairwise points where the two points are neighbors of each other. After the transformation, the considered pairwise points within the same class are as close as possible, while those between different classes are as far as possible. We formulate this problem as a constrained optimization problem, in which the global optimum can be effectively and efficiently obtained. Compared with the popular supervised method, Linear Discriminant Analysis (LDA), our method has three significant advantages. First, it is able to extract more discriminative features. Second, it can deal with the case where the class distributions aremore complex than Gaussian. Third, the singularity problem existing in LDA does not occur naturally. The performance on several data sets demonstrates the effectiveness of the proposed method.