Supervised optimal locality preserving projection

Authors:
W. K. Wong;H. T. Zhao
Affiliations:
Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong;Automation Department, East China University of Science and Technology, Shanghai, China
Venue:
Pattern Recognition
Year:
2012

Citing 15
Cited 4

The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
The CMU Pose, Illumination, and Expression Database

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast communication: Gabor feature-based face recognition using supervised locality preserving projection

Signal Processing
A tutorial on spectral clustering

Statistics and Computing
Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Locality sensitive discriminant analysis

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Face recognition using discriminant locality preserving projections

Image and Vision Computing
Letter: Supervised kernel locality preserving projections for face recognition

Neurocomputing
Orthogonal Laplacianfaces for Face Recognition

IEEE Transactions on Image Processing

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

Pattern Recognition
Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition

Neural Networks
Global plus local: A complete framework for feature extraction and recognition

Pattern Recognition
Supervised orthogonal discriminant subspace projects learning for face recognition

Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In the past few years, the computer vision and pattern recognition community has witnessed a rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among these methods, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Unlike the unsupervised learning scheme of LPP, this paper follows the supervised learning scheme, i.e. it uses both local information and class information to model the similarity of the data. Based on novel similarity, we propose two feature extraction algorithms, supervised optimal locality preserving projection (SOLPP) and normalized Laplacian-based supervised optimal locality preserving projection (NL-SOLPP). Optimal here means that the extracted features via SOLPP (or NL-SOLPP) are statistically uncorrelated and orthogonal. We compare the proposed SOLPP and NL-SOLPP with LPP, orthogonal locality preserving projection (OLPP) and uncorrelated locality preserving projection (ULPP) on publicly available data sets. Experimental results show that the proposed SOLPP and NL-SOLPP achieve much higher recognition accuracy.