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VLDB
2007
ACM
174views Database» more  VLDB 2007»
10 years 12 months ago
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
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...
Heng Tao Shen, Xiaofang Zhou, Aoying Zhou
KDD
2001
ACM
187views Data Mining» more  KDD 2001»
11 years 4 days ago
Random projection in dimensionality reduction: applications to image and text data
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however,...
Ella Bingham, Heikki Mannila
ICML
2004
IEEE
11 years 17 days ago
A kernel view of the dimensionality reduction of manifolds
Bernhard Schölkopf, Daniel D. Lee, Jihun Ham,...
ICML
2005
IEEE
11 years 17 days ago
Action respecting embedding
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...
Michael H. Bowling, Ali Ghodsi, Dana F. Wilkinson
ICML
2006
IEEE
11 years 17 days ago
The support vector decomposition machine
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...
Francisco Pereira, Geoffrey J. Gordon
ICIP
2000
IEEE
11 years 1 months ago
Dimensionality Reduction for Image Retrieval
Peng Wu, B. S. Manjunath, H. D. Shin
ICIP
2006
IEEE
11 years 1 months ago
Joint Dimensionality Reduction, Classification and Segmentation of Hyperspectral Images
Dimensionality reduction, spectral classification and segmentation are the three main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation appr...
Nadia Bali, Ali Mohammad-Djafari, Adel Mohammadpou...
CVPR
2008
IEEE
11 years 1 months ago
Dimensionality reduction by unsupervised regression
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...
Miguel Á. Carreira-Perpiñán, ...
CVPR
2007
IEEE
11 years 1 months ago
Integrating Global and Local Structures: A Least Squares Framework for Dimensionality Reduction
Linear Discriminant Analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximi...
Jianhui Chen, Jieping Ye, Qi Li
CVPR
2007
IEEE
11 years 1 months ago
Dimensionality Reduction and Clustering on Statistical Manifolds
Sang-Mook Lee, A. Lynn Abbott, Philip A. Araman
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