B0502
Title: Hierarchical tensor decompositions and applications
Authors: Jamie Haddock - Harvey Mudd College (United States) [presenting]
Abstract: Nonnegative matrix factorization (NMF) has found many applications, including topic modeling and document analysis. Hierarchical NMF (HNMF) variants are able to learn topics at various levels of granularity and illustrate their hierarchical relationship. Recently, nonnegative tensor factorization (NTF) methods have been applied similarly in order to handle data sets with complex, multi-modal structures. Hierarchical NTF (HNTF) methods have been proposed; however, these methods do not naturally generalize their matrix-based counterparts. We propose a new HNTF model which directly generalizes an HNMF model special case, and provide a supervised extension. Our experimental results show that this model more naturally illuminates the topic hierarchy than previous HNMF and HNTF methods.
