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NIPS
2008
13 years 11 months ago
Diffeomorphic Dimensionality Reduction
This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We argue that constraining the mapping between the high a...
Christian Walder, Bernhard Schölkopf
VLDB
2007
ACM
174views Database» more  VLDB 2007»
14 years 10 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
CAIP
1997
Springer
125views Image Analysis» more  CAIP 1997»
14 years 1 months ago
An Algorithm for Intrinsic Dimensionality Estimation
Abstract. In this paper a new method for analyzing the intrinsic dimensionality (ID) of low dimensional manifolds in high dimensional feature spaces is presented. The basic idea is...
Jörg Bruske, Gerald Sommer
SDM
2007
SIAM
133views Data Mining» more  SDM 2007»
13 years 11 months ago
On Point Sampling Versus Space Sampling for Dimensionality Reduction
In recent years, random projection has been used as a valuable tool for performing dimensionality reduction of high dimensional data. Starting with the seminal work of Johnson and...
Charu C. Aggarwal
TIT
2008
73views more  TIT 2008»
13 years 9 months ago
L-CAMP: Extremely Local High-Performance Wavelet Representations in High Spatial Dimension
A new wavelet-based methodology for representing data on regular grids is introduced and studied. The main attraction of this new L-CAMP methodology is in the way it scales with th...
Youngmi Hur, Amos Ron