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ICML
2004
IEEE
16 years 6 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
STEP
2003
IEEE
15 years 11 months ago
On Analysis of Design Component Contracts: A Case Study
Software patterns are a new design paradigm used to solve problems that arise when developing software within a particular context. Patterns capture the static and dynamic structu...
Jing Dong, Paulo S. C. Alencar, Donald D. Cowan
CORR
2007
Springer
167views Education» more  CORR 2007»
15 years 6 months ago
Optimal Solutions for Sparse Principal Component Analysis
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonze...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
NN
2008
Springer
201views Neural Networks» more  NN 2008»
15 years 6 months ago
Learning representations for object classification using multi-stage optimal component analysis
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. In multi-stage Optima...
Yiming Wu, Xiuwen Liu, Washington Mio
ICASSP
2011
IEEE
14 years 9 months ago
Video thumbnail extraction using video time density function and independent component analysis mixture model
In this paper, we propose a new vector quantization method to create video thumbnail. In particular, we employ video time density function (VTDF) to explore the temporal character...
Junfeng Jiang, Xiao-Ping Zhang