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Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
- Source :
- ICASSP
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from $\ell_{q,1}$ norm minimization algorithms in the multiple measurement vectors (MMV) model and utilizes joint-sparsity promoting hard thresholding for feature elimination. A regularization of the sample covariance matrix is also needed as we consider the challenging scenario where the number of features (variables) is comparable or exceeding the sample size of the training dataset. A simulation study and four examples of real-life microarray datasets evaluate the performances of CRDA based classifiers. Overall, the proposed method gives fewer misclassification errors than its competitors, while at the same time achieving accurate feature selection.<br />Comment: 5 pages, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing 15-20 April 2018 | Calgary, Alberta, Canada
- Subjects :
- FOS: Computer and information sciences
Clustering high-dimensional data
Machine Learning (stat.ML)
Feature selection
02 engineering and technology
Statistics - Applications
Regularization (mathematics)
Methodology (stat.ME)
Statistics - Machine Learning
0202 electrical engineering, electronic engineering, information engineering
Applications (stat.AP)
Statistics - Methodology
Sparse matrix
ta113
business.industry
Regularized discriminant analysis
Pattern recognition
Classification
Linear discriminant analysis
Thresholding
ComputingMethodologies_PATTERNRECOGNITION
Feature (computer vision)
Sample size determination
Gene expression microarrays
020201 artificial intelligence & image processing
Artificial intelligence
Joint-sparse recovery
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Accession number :
- edsair.doi.dedup.....b27ea98187f617f68c401496f552710d