10 search results - page 1 / 2 » Sampling-based dimension reduction for subspace approximatio... |
STOC
2007
ACM
14 years 5 months ago
2007
ACM
We give a randomized bi-criteria algorithm for the problem of finding a k-dimensional subspace that minimizes the Lp-error for given points, i.e., p-th root of the sum of p-th
JMLR
2010
12 years 11 months ago
2010
The goal of sufficient dimension reduction in supervised learning is to find the lowdimensional subspace of input features that is `sufficient' for predicting output values. ...
TSMC
2010
12 years 11 months ago
2010
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
SODA
2010
ACM
14 years 2 months ago
2010
ACM
We consider the problem of approximating a set P of n points in Rd by a j-dimensional subspace under the p measure, in which we wish to minimize the sum of p distances from each p...
CVPR
2007
IEEE
14 years 6 months ago
2007
IEEE
The success of tensor-based subspace learning depends heavily on reducing correlations along the column vectors of the mode-k flattened matrix. In this work, we study the problem ...