Graph optimization for dimensionality reduction with sparsity constraints

Authors:
Limei Zhang;Songcan Chen;Lishan Qiao
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, 210016 Nanjing, PR China and Department of Mathematics Science, Liaocheng University, 252000 Liaoc ...;Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, 210016 Nanjing, PR China;Department of Mathematics Science, Liaocheng University, 252000 Liaocheng, PR China
Venue:
Pattern Recognition
Year:
2012

Citing 15
Cited 5

PCA versus LDA

IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence of a block coordinate descent method for nondifferentiable minimization

Journal of Optimization Theory and Applications
A generalized Foley-Sammon transform based on generalized fisher discriminant criterion and its application to face recognition

Pattern Recognition Letters
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Acquiring Linear Subspaces for Face Recognition under Variable Lighting

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph construction and b-matching for semi-supervised learning

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Semi-supervised orthogonal discriminant analysis via label propagation

Pattern Recognition
Sparsity preserving projections with applications to face recognition

Pattern Recognition
Trace ratio problem revisited

IEEE Transactions on Neural Networks
Sparsity preserving discriminant analysis for single training image face recognition

Pattern Recognition Letters
Graph-optimized locality preserving projections

Pattern Recognition
Orthogonal Laplacianfaces for Face Recognition

IEEE Transactions on Image Processing

Constrained large Margin Local Projection algorithms and extensions for multimodal dimensionality reduction

Pattern Recognition
Using the original and 'symmetrical face' training samples to perform representation based two-step face recognition

Pattern Recognition
Large Margin Subspace Learning for feature selection

Pattern Recognition
Low-rank representation with local constraint for graph construction

Neurocomputing
Double linear regressions for single labeled image per person face recognition

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

Graph-based dimensionality reduction (DR) methods play an increasingly important role in many machine learning and pattern recognition applications. In this paper, we propose a novel graph-based learning scheme to conduct Graph Optimization for Dimensionality Reduction with Sparsity Constraints (GODRSC). Different from most of graph-based DR methods where graphs are generally constructed in advance, GODRSC aims to simultaneously seek a graph and a projection matrix preserving such a graph in one unified framework, resulting in an automatically updated graph. Moreover, by applying an l"1 regularizer, a sparse graph is achieved, which models the ''locality'' structure of data and contains natural discriminating information. Finally, extensive experiments on several publicly available UCI and face databases verify the feasibility and effectiveness of the proposed method.