Title:
SqueezeFit: Label-aware dimensionality reduction by semidefinite programming
Abstract:
Given labeled points in a high-dimensional vector space, we seek a low-dimensional subspace such that that projecting onto this subspace maintains some prescribed distance between points of differing labels. Intended applications include compressive classification. This talk will introduce a semidefinite relaxation of this problem, along with various performance guarantees. (Joint work with Culver McWhirter (OSU) and Soledad Villar (NYU).)
|