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TSMC
2008
182views more  TSMC 2008»
9 years 10 months ago
Incremental Linear Discriminant Analysis for Face Recognition
Abstract--Dimensionality reduction methods have been successfully employed for face recognition. Among the various dimensionality reduction algorithms, linear (Fisher) discriminant...
Haitao Zhao, Pong Chi Yuen
PAMI
2007
154views more  PAMI 2007»
9 years 10 months ago
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemming from statistics or geometry theory—has been designed to provide different soluti...
Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zha...
VLSISP
2002
139views more  VLSISP 2002»
9 years 10 months ago
A Modified Minimum Classification Error (MCE) Training Algorithm for Dimensionality Reduction
Dimensionality reduction is an important problem in pattern recognition. There is a tendency of using more and more features to improve the performance of classifiers. However, not...
Xuechuan Wang, Kuldip K. Paliwal
TNN
2008
105views more  TNN 2008»
9 years 10 months ago
Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection
Abstract--High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is ...
Shuiwang Ji, Jieping Ye
BMCBI
2005
120views more  BMCBI 2005»
9 years 10 months ago
SpectralNET - an application for spectral graph analysis and visualization
Background: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks ...
Joshua J. Forman, Paul A. Clemons, Stuart L. Schre...
ENGL
2007
101views more  ENGL 2007»
9 years 10 months ago
Multiresolution Knowledge Mining using Wavelet Transform
— Most research in Knowledge Mining deal with the basic models like clustering, classification, regression, association rule mining and so on. In the process of quest for knowled...
R. Pradeep Kumar, P. Nagabhushan
JMLR
2006
148views more  JMLR 2006»
9 years 10 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
ICML
2010
IEEE
9 years 11 months ago
Local Minima Embedding
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
Minyoung Kim, Fernando De la Torre
NIPS
1998
9 years 12 months ago
Restructuring Sparse High Dimensional Data for Effective Retrieval
The task in text retrieval is to find the subset of a collection of documents relevant to a user's information request, usually expressed as a set of words. Classically, docu...
Charles Lee Isbell Jr., Paul A. Viola
VISSYM
2003
10 years 3 hour ago
Visual Hierarchical Dimension Reduction for Exploration of High Dimensional Datasets
Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, glyphs, and scatterplot matrices, do not scale well to high numbers of dimension...
Jing Yang, Matthew O. Ward, Elke A. Rundensteiner,...
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