49 search results - page 3 / 10 » Dimensionality Reduction by Learning an Invariant Mapping |
153
Voted
ICML
2004
IEEE
15 years 9 months ago
2004
IEEE
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
138
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SPEECH
1998
15 years 3 months ago
1998
We consider the problem of obtaining a reduced dimension representation of electropalatographic (EPG) data. An unsupervised learning approach based on latent variable modelling is...
159
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IJCAI
2003
15 years 5 months ago
2003
We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
126
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CVPR
2010
IEEE
15 years 11 months ago
2010
IEEE
We introduce a parametric version (pDRUR) of the recently proposed Dimensionality Reduction by Unsupervised Regression algorithm. pDRUR alternately minimizes reconstruction error ...
124
Voted
ICPR
2010
IEEE
15 years 6 months ago
2010
IEEE
—A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach,...