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CVPR
2010
IEEE

Parametric Dimensionality Reduction by Unsupervised Regression

13 years 11 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 by fitting parametric functions given latent coordinates and data, and by updating latent coordinates given functions (with a Gauss-Newton method decoupled over coordinates). Both the fit and the update become much faster while attaining results of similar quality, and afford dealing with far larger datasets (105 points). We show in a number of benchmarks how the algorithm efficiently learns good latent coordinates and bidirectional mappings between the data and latent space, even with very noisy or low-quality initializations, often drastically improving the result of spectral and other methods. We consider the problem of dimensionality reduction, where given a high-dimensional dataset of N points in D dimensions YD×N = (y1, . . . , yN ), we want to estimate mappings F : y → x (dimensionality reduction...
Miguel Carreira-perpinan, Zhengdong Lu
Added 21 Apr 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Miguel Carreira-perpinan, Zhengdong Lu
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