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ICPR
2008
IEEE
16 years 5 months ago
Feature selection focused within error clusters
We propose a feature selection method that constructs each new feature by analysis of tight error clusters. This is a greedy, time-efficient forward selection algorithm that itera...
Henry S. Baird, Sui-Yu Wang
KDD
2012
ACM
197views Data Mining» more  KDD 2012»
13 years 6 months ago
On the separability of structural classes of communities
Three major factors govern the intricacies of community extraction in networks: (1) the application domain includes a wide variety of networks of fundamentally different natures,...
Bruno D. Abrahao, Sucheta Soundarajan, John E. Hop...
CVPR
2008
IEEE
16 years 6 months ago
A unified framework for generalized Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is one of the wellknown methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope w...
Shuiwang Ji, Jieping Ye
SODA
2004
ACM
110views Algorithms» more  SODA 2004»
15 years 5 months ago
Probabilistic analysis of knapsack core algorithms
We study the average-case performance of algorithms for the binary knapsack problem. Our focus lies on the analysis of so-called core algorithms, the predominant algorithmic conce...
René Beier, Berthold Vöcking
JMLR
2006
148views more  JMLR 2006»
15 years 4 months ago
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
Dimensionality reduction is an important pre-processing step in many applications. Linear discriminant analysis (LDA) is a classical statistical approach for supervised dimensiona...
Jieping Ye, Tao Xiong