70 search results - page 7 / 14 » Bypassing the embedding: algorithms for low dimensional metr... |
BMVC
2010
14 years 7 months ago
2010
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
ICML
2004
IEEE
15 years 3 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 ...
103
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CVPR
2010
IEEE
15 years 5 months ago
2010
IEEE
Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been show...
113
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AMFG
2003
IEEE
15 years 2 months ago
2003
IEEE
In this paper, we propose the concept of Manifold of Facial Expression based on the observation that images of a subject’s facial expressions define a smooth manifold in the hig...
PAMI
2006
14 years 9 months ago
2006
We provide evidence that non-linear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define ...