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ICML
2007
IEEE
14 years 9 months ago
Adaptive dimension reduction using discriminant analysis and K-means clustering
We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to ...
Chris H. Q. Ding, Tao Li
ICDM
2006
IEEE
193views Data Mining» more  ICDM 2006»
14 years 2 months ago
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features
Several spatio-temporal data collected in many applications, such as fMRI data in medical applications, can be represented as a Multivariate Time Series (MTS) matrix with m rows (...
Hyunjin Yoon, Cyrus Shahabi
CORR
2010
Springer
221views Education» more  CORR 2010»
13 years 5 months ago
Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition
In this paper we describe a procedure to reduce the size of the input feature vector. A complex pattern recognition problem like face recognition involves huge dimension of input ...
Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nas...
JMLR
2008
131views more  JMLR 2008»
13 years 8 months ago
On Relevant Dimensions in Kernel Feature Spaces
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
CVPR
2010
IEEE
13 years 7 months ago
Transform Coding for Fast Approximate Nearest Neighbor Search in High Dimensions
We examine the problem of large scale nearest neighbor search in high dimensional spaces and propose a new approach based on the close relationship between nearest neighbor search...
Jonathan Brandt