Incremental Sparse Binary Vector Similarity Search in High-Dimensional Space

Kirill Levchenko, Justin Ma, Zhen Xiao and Yin Zhang
CS2006-0866
September 26, 2006

Given a sparse binary matrix A and a sparse query vector x, can we efficiently identify the large entries of the matrix-vector product Ax? This problem occurs in document comparison, spam filtering, network intrusion detection, information retrieval, as well as other areas. We present an exact deterministic algorithm that takes advantage of the sparseness of A and x. Although in the worst case, the query complexity is linear in the number of rows of A, the amortized query complexity for a sequence of several similar queries depends only logarithmically on the size of A when the non-zero entries of A and the queries are distributed uniformly.


How to view this document


The authors of these documents have submitted their reports to this technical report series for the purpose of non-commercial dissemination of scientific work. The reports are copyrighted by the authors, and their existence in electronic format does not imply that the authors have relinquished any rights. You may copy a report for scholarly, non-commercial purposes, such as research or instruction, provided that you agree to respect the author's copyright. For information concerning the use of this document for other than research or instructional purposes, contact the authors. Other information concerning this technical report series can be obtained from the Computer Science and Engineering Department at the University of California at San Diego, techreports@cs.ucsd.edu.


[ Search ]


NCSTRL
This server operates at UCSD Computer Science and Engineering.
Send email to webmaster@cs.ucsd.edu