Theory of linear and integer programming
Theory of linear and integer programming
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Novel Unsupervised Feature Filtering of Biological Data
Bioinformatics
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Bioinformatics
Supervised dimensionality reduction via sequential semidefinite programming
Pattern Recognition
Feature selection with redundancy-constrained class separability
IEEE Transactions on Neural Networks
Structured Variable Selection with Sparsity-Inducing Norms
The Journal of Machine Learning Research
Identifying critical variables of principal components for unsupervised feature selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
l2,1-norm regularized discriminative feature selection for unsupervised learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
| Hi-index | 0.07 |
Many approaches have been developed for dimensionality reduction. These approaches can broadly be categorized into supervised and unsupervised methods. In case of supervised dimensionality reduction, for any input vector the target value is known, which can be a class label also. In a supervised approach, our objective is to select a subset of features that has adequate discriminating power to predict the target value. This target value for an input vector is absent in case of an unsupervised approach. In an unsupervised scheme, we mainly try to find a subset that can capture the inherent ''structure'' of the data, such as the neighborhood relation or the cluster structure. In this work, we first study a Singular Value Decomposition (SVD) based unsupervised feature selection approach proposed by Varshavsky et al. Then we propose a modification of this method to improve its performance. An SVD-entropy based supervised feature selection algorithm is also developed in this paper. Performance evaluation of the algorithms is done on altogether 13 benchmark and one Synthetic data sets. The quality of the selected features is assessed using three indices: Sammon's Error (SE), Cluster Preservation Index (CPI) and MisClassification Error (MCE) using a 1-Nearest Neighbor (1-NN) classifier. Besides showing the improvement of the modified unsupervised scheme over the existing one, we have also made a comparative study of the modified unsupervised and the proposed supervised algorithms with one well-known unsupervised and two popular supervised feature selection methods respectively. Our results reveal the effectiveness of the proposed algorithms in selecting relevant features.