A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Continuous characterizations of the maximum clique problem
Mathematics of Operations Research
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class discovery in gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
A Factorization Approach to Grouping
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Machine Learning
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature selection and feature extraction for text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Gene Selection via a Spectral Approach
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A new graph-theoretic approach to clustering and segmentation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Information Sciences: an International Journal
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Unsupervised feature selection for principal components analysis
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
An evaluation of dimension reduction techniques for one-class classification
Artificial Intelligence Review
An improved approximation algorithm for the column subset selection problem
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Acoustic feature selection for automatic emotion recognition from speech
Information Processing and Management: an International Journal
Feature Selection for Local Learning Based Clustering
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An Iterative Hybrid Filter-Wrapper Approach to Feature Selection for Document Clustering
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Functional Feature Selection by Weighted Projections in Pathological Voice Detection
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Feature extraction of weighted data for implicit variable selection
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Weighted feature extraction with a functional data extension
Neurocomputing
On the relevance of linear discriminative features
Information Sciences: an International Journal
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
Discriminative codeword selection for image representation
Proceedings of the international conference on Multimedia
A novel stability based feature selection framework for k-means clustering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
A New Unsupervised Feature Ranking Method for Gene Expression Data Based on Consensus Affinity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Unsupervised feature selection for linked social media data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering
Computer Methods and Programs in Biomedicine
Self-taught dimensionality reduction on the high-dimensional small-sized data
Pattern Recognition
Unsupervised Feature Selection with Feature Clustering
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
G-Optimal Feature Selection with Laplacian regularization
Neurocomputing
A scatter method for data and variable importance evaluation
Integrated Computer-Aided Engineering
Feature selection for k-means clustering stability: theoretical analysis and an algorithm
Data Mining and Knowledge Discovery
Hi-index | 0.00 |
The problem of selecting a subset of relevant features in a potentially overwhelming quantity of data is classic and found in many branches of science. Examples in computer vision, text processing and more recently bio-informatics are abundant. In text classification tasks, for example, it is not uncommon to have 104 to 107 features of the size of the vocabulary containing word frequency counts, with the expectation that only a small fraction of them are relevant. Typical examples include the automatic sorting of URLs into a web directory and the detection of spam email.In this work we present a definition of "relevancy" based on spectral properties of the Laplacian of the features' measurement matrix. The feature selection process is then based on a continuous ranking of the features defined by a least-squares optimization process. A remarkable property of the feature relevance function is that sparse solutions for the ranking values naturally emerge as a result of a "biased non-negativity" of a key matrix in the process. As a result, a simple least-squares optimization process converges onto a sparse solution, i.e., a selection of a subset of features which form a local maximum over the relevance function. The feature selection algorithm can be embedded in both unsupervised and supervised inference problems and empirical evidence show that the feature selections typically achieve high accuracy even when only a small fraction of the features are relevant.