VLDB
2007
ACM
14 years 8 months ago
2007
ACM
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
KDD
2001
ACM
14 years 9 months ago
2001
ACM
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however,...
ICML
2004
IEEE
14 years 9 months ago
2004
IEEE
ICML
2005
IEEE
14 years 9 months ago
2005
IEEE
Dimensionality reduction is the problem of finding a low-dimensional representation of highdimensional input data. This paper examines the case where additional information is kno...
ICML
2006
IEEE
14 years 9 months ago
2006
IEEE
In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learni...
ICIP
2000
IEEE
14 years 10 months ago
2000
IEEE
ICIP
2006
IEEE
14 years 10 months ago
2006
IEEE
Dimensionality reduction, spectral classification and segmentation are the three main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation appr...
CVPR
2008
IEEE
14 years 10 months ago
2008
IEEE
We consider the problem of dimensionality reduction, where given high-dimensional data we want to estimate two mappings: from high to low dimension (dimensionality reduction) and f...
CVPR
2007
IEEE
14 years 10 months ago
2007
IEEE
Linear Discriminant Analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximi...
CVPR
2007
IEEE
14 years 10 months ago
2007
IEEE
