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» Constrained Laplacian Eigenmap for dimensionality reduction
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CVPR
2008
IEEE
16 years 1 months ago
Dimensionality reduction by unsupervised regression
We consider the problem of dimensionality reduction, where given high-dimensional data we want to estimate two mappings: from high to low dimension (dimensionality reduction) and f...
Miguel Á. Carreira-Perpiñán, ...
AAAI
2010
15 years 21 days 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
AAAI
2007
15 years 1 months ago
Isometric Projection
Recently the problem of dimensionality reduction has received a lot of interests in many fields of information processing. We consider the case where data is sampled from a low d...
Deng Cai, Xiaofei He, Jiawei Han
CVPR
2010
IEEE
15 years 7 months ago
Parametric Dimensionality Reduction by Unsupervised Regression
We introduce a parametric version (pDRUR) of the recently proposed Dimensionality Reduction by Unsupervised Regression algorithm. pDRUR alternately minimizes reconstruction error ...
Miguel Carreira-perpinan, Zhengdong Lu
NIPS
2003
15 years 18 days ago
Non-linear CCA and PCA by Alignment of Local Models
We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of...
Jakob J. Verbeek, Sam T. Roweis, Nikos A. Vlassis