Discriminant Analysis Methods for Microarray Data Classification

Authors:
Chuanliang Chen;Yun-Chao Gong;Rongfang Bie
Affiliations:
Department of Computer Science, Beijing Normal University, Beijing, China 100875;Software Institute, Nanjing University, Nanjing, China 210089;Department of Computer Science, Beijing Normal University, Beijing, China 100875
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

Citing 6
Cited 0

Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Approximate geodesic distances reveal biologically relevant structures in microarray data

Bioinformatics
Neighborhood Preserving Embedding

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis

IEEE Transactions on Knowledge and Data Engineering
Training Linear Discriminant Analysis in Linear Time

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Locality sensitive discriminant analysis

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

The studies of DNA Microarray technologies have produced high-dimensional data. In order to alleviate the "curse of dimensionality" and better analyze these data, many linear and non-linear dimension reduction methods such as PCA and LLE have been widely studied. In this paper, we report our work on microarray data classification with three latest proposed discriminant analysis methods: Locality Sensitive Discriminant Analysis (LSDA), Spectral Regression Discriminant Analysis (SRDA), and Supervised Neighborhood Preserving Embedding (S-NPE). Results of experiments on four data sets show the excellent effectiveness and efficiency of SRDA.