Sciweavers

149 search results - page 14 / 30
» Semi-supervised nonlinear dimensionality reduction
Sort
View
109
Voted
VC
2010
177views more  VC 2010»
15 years 1 months ago
Color-to-gray conversion using ISOMAP
In this paper we present a new algorithm to transform an RGB color image to a grayscale image. We propose using non-linear dimension reduction techniques to map higher dimensional ...
Ming Cui, Jiuxiang Hu, Anshuman Razdan, Peter Wonk...
AAAI
2010
15 years 4 months ago
Conformal Mapping by Computationally Efficient Methods
Dimensionality reduction is the process by which a set of data points in a higher dimensional space are mapped to a lower dimension while maintaining certain properties of these p...
Stefan Pintilie, Ali Ghodsi
IVC
2007
184views more  IVC 2007»
15 years 3 months ago
Image distance functions for manifold learning
Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of bas...
Richard Souvenir, Robert Pless
NIPS
2001
15 years 4 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
ICIP
2007
IEEE
15 years 7 months ago
Unsupervised Nonlinear Manifold Learning
This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables t...
Matthieu Brucher, Christian Heinrich, Fabrice Heit...