Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Manifold-adaptive dimension estimation
Proceedings of the 24th international conference on Machine learning
Probability-Based Locally Linear Embedding for Classification
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Feature extraction using constrained maximum variance mapping
Pattern Recognition
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
A New Orthogonal Discriminant Projection Based Prediction Method for Bioinformatic Data
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Locally Linear Discriminant Embedding for Tumor Classification
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Supervised locally linear embedding with probability-based distance for classification
Computers & Mathematics with Applications
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Constrained maximum variance mapping for tumor classification
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Computers in Biology and Medicine
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An important application of gene expression data is tumor classification. Dimensionality reduction is a key step of tumor classification, as gene expression data is of high dimensionality and small sample size (SSS) and it contains a large number of redundant genes irrelevant to tumor phenotypes. Manifold learning is an excellent tool for dimensionality reduction and it is promising for gene expression data analysis. In this paper, an improved supervised orthogonal discriminant projection (SODP) is proposed for tumor classification. In SODP, an effective weight measurement between two nodes of the weight graph is designed according to both sample class information and local information. With the novel measurement, SODP can maximize the weighted difference between the non-local scatter and the local scatter, on the basis of locality preserving. The experimental results with five public tumor datasets demonstrate that the proposed SODP is quite efficient and feasible for tumor classification.