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
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. ...
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...
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...
Dan Feldman, Morteza Monemizadeh, Christian Sohler...
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 ...
Shuicheng Yan, Dong Xu, Stephen Lin, Thomas S. Hua...