98 search results - page 2 / 20 » Regression on manifolds using kernel dimension reduction |
90
Voted
ICML
2010
IEEE
15 years 21 days ago
2010
IEEE
Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensiona...
100
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ICCV
2009
IEEE
16 years 4 months ago
2009
IEEE
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
104
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ICIP
2008
IEEE
16 years 1 months ago
2008
IEEE
This paper uses a recently introduced manifold kernel regression technique to explore the relationship between facial shape and attractiveness on a heterogeneous dataset of over t...
IDA
2009
Springer
15 years 6 months ago
2009
Springer
Abstract. Joint diagonalization for ICA is often performed on the orthogonal group after a pre-whitening step. Here we assume that we only want to extract a few sources after pre-w...
133
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TSMC
2010
14 years 6 months ago
2010
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...