Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of gene-expression data: The manifold-based metric learning way
Pattern Recognition
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Supervised locally linear embedding with probability-based distance for classification
Computers & Mathematics with Applications
Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis
Expert Systems with Applications: An International Journal
Learning a locality discriminating projection for classification
Knowledge-Based Systems
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A new manifold learning method, called improved semi-supervised local fisher discriminant analysis (iSELF), for gene expression data classification is proposed. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution and it can be computed based on Eigen decompositions. Experiments on synthetic data and SRBCT, DLBCL and brain tumor gene expression datasets are performed to test and evaluate the proposed method. The experimental results and comparisons demonstrate the effectiveness of the proposed method.