Supervised locally linear embedding with probability-based distance for classification

Authors:
Lingxiao Zhao;Zhenyue Zhang
Affiliations:
Department of Mathematics and State Key Lab of CAD&CG, Zhejiang University, Yuquan Campus, Hangzhou 310027, PR China;Department of Mathematics and State Key Lab of CAD&CG, Zhejiang University, Yuquan Campus, Hangzhou 310027, PR China
Venue:
Computers & Mathematics with Applications
Year:
2009

Citing 6
Cited 7

The nature of statistical learning theory

The nature of statistical learning theory
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

SIAM Journal on Scientific Computing
Classification of gene-expression data: The manifold-based metric learning way

Pattern Recognition
Probability-Based Locally Linear Embedding for Classification

FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Supervised locally linear embedding

ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing

Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data

Neurocomputing
Locally linear embedding: a survey

Artificial Intelligence Review
Gene expression data classification based on improved semi-supervised local Fisher discriminant analysis

Expert Systems with Applications: An International Journal
Gene Classification Using Parameter-Free Semi-Supervised Manifold Learning

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Enhanced semi-supervised local Fisher discriminant analysis for face recognition

Future Generation Computer Systems
Dimensionality reduction-based spoken emotion recognition

Multimedia Tools and Applications
A supervised orthogonal discriminant projection for tumor classification using gene expression data

Computers in Biology and Medicine

Quantified Score

Hi-index	0.09

Visualization

Abstract

We present a novel dimension reduction method for classification based on probability-based distance and the technique of locally linear embedding (LLE). Logistic Discrimination (LD) is adopted for estimating the probability distribution as well as for classification on the reduced data. Different from the supervised locally linear embedding (SLLE) that is only used for the dimension reduction of training data, our probability-based locally linear embedding (PLLE) can be applied on both training and testing data. Five microarray data sets in high-dimensional spaces, the IRIS data, and a real set of handwritten digits are experimented. The numerical results show the proposed methodology performs better, compared with the LD classifiers applied on the lower-dimensional embedding coordinates computed by LLE or SLLE.