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» Dimensionality Reduction of Clustered Data Sets
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NIPS
2008
13 years 9 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
SDM
2007
SIAM
126views Data Mining» more  SDM 2007»
13 years 8 months ago
Nonlinear Dimensionality Reduction using Approximate Nearest Neighbors
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-d...
Erion Plaku, Lydia E. Kavraki
SDM
2007
SIAM
133views Data Mining» more  SDM 2007»
13 years 8 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
ACMSE
2005
ACM
14 years 1 months ago
Using nonlinear dimensionality reduction in 3D figure animation
This paper explores a method for re-sequencing an existing set of animation, specifically motion capture data, to generate new motion. Re-using animation is helpful in designing ...
A. Elizabeth Seward, Bobby Bodenheimer
NIPS
2001
13 years 8 months ago
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
Mikhail Belkin, Partha Niyogi