148 search results - page 13 / 30 » Riemannian Manifold Learning for Nonlinear Dimensionality Re... |
NIPS
2001
13 years 9 months ago
2001
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...
PAKDD
2010
ACM
13 years 5 months ago
2010
ACM
Data mining tasks results are usually improved by reducing the dimensionality of data. This improvement however is achieved harder in the case that data lay on a non linear manifol...
ICIP
2005
IEEE
14 years 9 months ago
2005
IEEE
We study the use of kernel subspace methods that learn low-dimensional subspace representations for classification tasks. In particular, we propose a new method called kernel weigh...
ICMCS
2005
IEEE
14 years 1 months ago
2005
IEEE
Semi-definite Embedding (SDE) has been a recently proposed to maximize the sum of pair wise squared distances between outputs while the input data and outputs are locally isometri...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...