Title :
Random projection trees for vector quantization
Author :
Dasgupta, Sanjoy ; Freund, Yoav
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of California, San Diego, CA
Abstract :
A simple and computationally efficient scheme for tree-structured vector quantization is presented. Unlike previous methods, its quantization error depends only on the intrinsic dimension of the data distribution, rather than the apparent dimension of the space in which the data happen to lie.
Keywords :
trees (mathematics); vector quantisation; data distribution; random projection trees; vector quantization; Computational complexity; Computer science; Error analysis; Machine learning; Manifolds; Power engineering and energy; Source coding; Statistical analysis; Statistics; Vector quantization; Vector quantization; computational complexity; manifolds; random projection; source coding;
Conference_Titel :
Communication, Control, and Computing, 2008 46th Annual Allerton Conference on
Conference_Location :
Urbana-Champaign, IL
Print_ISBN :
978-1-4244-2925-7
Electronic_ISBN :
978-1-4244-2926-4
DOI :
10.1109/ALLERTON.2008.4797555
