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TNN
2010
154views Management» more  TNN 2010»
12 years 10 months ago
Discriminative semi-supervised feature selection via manifold regularization
We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number ...
Zenglin Xu, Irwin King, Michael R. Lyu, Rong Jin
TCSV
2010
12 years 10 months ago
Image and Video Segmentation by Combining Unsupervised Generalized Gaussian Mixture Modeling and Feature Selection
In this letter, we propose a clustering model that efficiently mitigates image and video under/over-segmentation by combining generalized Gaussian mixture modeling and feature sele...
Mohand Saïd Allili, Djemel Ziou, Nizar Bougui...
JMLR
2010
161views more  JMLR 2010»
12 years 10 months ago
Feature Selection for Text Classification Based on Gini Coefficient of Inequality
A number of feature selection mechanisms have been explored in text categorization, among which mutual information, information gain and chi-square are considered most effective. ...
Sanasam Ranbir Singh, Hema A. Murthy, Timothy A. G...
JMLR
2010
120views more  JMLR 2010»
12 years 10 months ago
Effective Wrapper-Filter hybridization through GRASP Schemata
Of all of the challenges which face the selection of relevant features for predictive data mining or pattern recognition modeling, the adaptation of computational intelligence tec...
Mohamed Amir Esseghir
JMLR
2010
165views more  JMLR 2010»
12 years 10 months ago
Feature Selection: An Ever Evolving Frontier in Data Mining
The rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities in production, services, comm...
Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao
JMLR
2010
108views more  JMLR 2010»
12 years 10 months ago
Feature Selection using Multiple Streams
Feature selection for supervised learning can be greatly improved by making use of the fact that features often come in classes. For example, in gene expression data, the genes wh...
Paramveer S. Dhillon, Dean P. Foster, Lyle H. Unga...
FUIN
2010
114views more  FUIN 2010»
12 years 10 months ago
Feature Selection via Maximizing Fuzzy Dependency
Feature selection is an important preprocessing step in pattern analysis and machine learning. The key issue in feature selection is to evaluate quality of candidate features. In t...
Qinghua Hu, Pengfei Zhu, Jinfu Liu, Yongbin Yang, ...
JMLR
2011
192views more  JMLR 2011»
12 years 10 months ago
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an...
Paramveer S. Dhillon, Dean P. Foster, Lyle H. Unga...
CORR
2011
Springer
200views Education» more  CORR 2011»
12 years 10 months ago
Using Feature Weights to Improve Performance of Neural Networks
Different features have different relevance to a particular learning problem. Some features are less relevant; while some very important. Instead of selecting the most relevant fe...
Ridwan Al Iqbal
3DOR
2010
12 years 10 months ago
Learning the Compositional Structure of Man-Made Objects for 3D Shape Retrieval
While approaches based on local features play a more and more important role for 3D shape retrieval, the problems of feature selection and similarity measurement between sets of l...
Raoul Wessel, Reinhard Klein