Open AccessProceedings Article
Trace ratio criterion for feature selection
Feiping Nie,Shiming Xiang,Yangqing Jia,Changshui Zhang,Shuicheng Yan +4 more
- pp 671-676
TLDR
A novel algorithm is proposed to efficiently find the global optimal feature subset such that the subset-level score is maximized, and extensive experiments demonstrate the effectiveness of the proposed algorithm in comparison with the traditional methods for feature selection.Abstract:
Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a feature subset is selected based on the corresponding score (subset-level score), which is calculated in a trace ratio form. Since the number of all possible feature subsets is very huge, it is often prohibitively expensive in computational cost to search in a brute force manner for the feature subset with the maximum subset-level score. Instead of calculating the scores of all the feature subsets, traditional methods calculate the score for each feature, and then select the leading features based on the rank of these feature-level scores. However, selecting the feature subset based on the feature-level score cannot guarantee the optimum of the subset-level score. In this paper, we directly optimize the subset-level score, and propose a novel algorithm to efficiently find the global optimal feature subset such that the subset-level score is maximized. Extensive experiments demonstrate the effectiveness of our proposed algorithm in comparison with the traditional methods for feature selection.read more
Citations
More filters
Journal ArticleDOI
Feature Selection: A Data Perspective
TL;DR: This survey revisits feature selection research from a data perspective and reviews representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data, and categorizes them into four main groups: similarity- based, information-theoretical-based, sparse-learning-based and statistical-based.
Proceedings ArticleDOI
l 2,1 -norm regularized discriminative feature selection for unsupervised learning
TL;DR: In this paper, a joint framework for unsupervised feature selection is proposed to select the most discriminative feature subset from the whole feature set in batch mode, where the class label of input data can be predicted by a linear classifier.
Proceedings Article
Generalized Fisher score for feature selection
TL;DR: In this paper, a generalized Fisher score was proposed to jointly select features, which maximizes the lower bound of traditional Fisher score by solving a quadratically constrained linear programming (QCLP) problem.
Journal ArticleDOI
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
TL;DR: This paper proposes a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learned with sparse regression to perform feature selection.
Journal ArticleDOI
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
TL;DR: A new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking and a semi- supervised long-term RF algorithm to refine the multimedia data representation.
References
More filters
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Proceedings ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.