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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Convex NMF on nonconvex massiv data
 Atzmüller, M.: LWA 2010  Lernen, Wissen und Adaptivität : Workshop Proceedings. Kassel, October 4  6, 2010 Kassel, 2010 S.97104 
 Workshop Lernen, Wissensentdeckung und Adaptivität (LWA) <2010, Kassel> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IAIS () 
Abstract
We present an extension of convexhull nonnegative matrix factorization (CHNMF) which was recently proposed as a large scale variant of convex nonnegative matrix factorization (CNMF) or Archetypal Analysis (AA). CHNMF factorizes a nonnegative data matrix V into two nonnegative matrix factors V WH such that the columns of W are convex combinations of certain data points so that they are readily interpretable to data analysts. There is, however, no free lunch: imposing convexity constraints on W typically prevents adaptation to intrinsic, low dimensional structures in the data. Alas, in cases where the data is distributed in a nonconvex manner or consists of mixtures of lower dimensional convex distributions, the cluster representatives obtained from CHNMF will be less meaningful. In this paper, we present a hierarchical CHNMF that automatically adapts to internal structures of a dataset, hence it yields meaningful and interpretable clusters for nonconvex datasets . This is also conformed by our extensive evaluation on DBLP publication records of 760,000 authors, 4,000,000 images harvested from the web, and 150,000,000 votes on World of Warcraft guilds.