Unmixing Incoherent Structures of Big Data by Randomized or Greedy Decomposition

Download Now
Provided by: Cornell University
Topic: Big Data
Format: PDF
Learning big data by matrix decomposition always suffers from expensive computation, mixing of complicated structures and noise. In this paper, the authors study more adaptive models and efficient algorithms that decompose a data matrix as the sum of semantic components with incoherent structures. They firstly introduce \"GO Decomposition (GoDec)\", an alternating projection method estimating the low-rank part L and the sparse part S from data matrix X = L + S + G corrupted by noise G. Two acceleration strategies are proposed to obtain scalable unmixing algorithm on big data: Bilateral Random Projection (BRP) is developed to speed up the update of L in GoDec by a closed-form built from left and right random projections of X-S in lower dimensions.
Download Now

Find By Topic