Compressive Spectral Embedding: Sidestepping the SVD
Abstract
Spectral embedding based on the Singular Value Decomposition (SVD) is a widely used preprocessing step in many learning tasks, typically leading to dimensionality reduction by projecting onto a number of dominant singular vectors and rescaling the coordinate axes (by a predefined function of the singular value). However, the number of such vectors required to capture problem structure grows with problem size, and even partial SVD computation becomes a bottleneck. In this paper, we propose a low-complexity compressive spectral embedding algorithm, which employs random projections and finite order polynomial expansions to compute approximations to SVD-based embedding.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 28, 2015
- Accession Number
- AD1019311
Entities
People
- Dinesh Ramasamy
- Upamanyu Madhow
Organizations
- University of California, Santa Barbara