Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel Methods for Subset Selection with Respect to Problem Knowledge
IEEE Intelligent Systems
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Regression: A Unified Approach for Sparse Subspace Learning
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Trace ratio criterion for feature selection
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Unified Solution to Nonnegative Data Factorization Problems
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Some filter methods stemming from statistics or geometry theory select features individually. Hence they neglect the combination of features and lead to suboptimal subset of features. To address this problem, a joint feature weights learning framework, which automatically determines the optimal size of the feature subset and selects the best features corresponding to a given adjacency graph, is proposed in this paper. In particular, our framework imposes nonnegative and l"2^2-norm constraints on feature weights and iteratively learns feature weights jointly and simultaneously. A new minimization algorithm with proved convergence is also developed to optimize the non-convex objective function. Utilizing this framework as a tool, we propose a new unsupervised feature selection algorithm called Joint Laplacian Feature Weights Learning. Experimental results on five real-world datasets demonstrate the effectiveness of our algorithm.