---
res:
bibo_abstract:
- We describe an algorithm that performs regularized non-negative matrix factorization
(NMF) to find independent components in non-negative data. Previous techniques
proposed for this purpose require the data to be grounded, with support that goes
down to 0 along each dimension. In our work, this requirement is eliminated. Based
on it, we present a technique to find a low-dimensional decomposition of spectrograms
by casting it as a problem of discovering independent non-negative components
from it. The algorithm itself is implemented as regularized non-negative matrix
factorization (NMF). Unlike other ICA algorithms, this algorithm computes the
mixing matrix rather than an unmixing matrix. This algorithm provides a better
decomposition than standard NMF when the underlying sources are independent. It
makes better use of additional observation streams than previous non-negative
ICA algorithms.@eng
bibo_authorlist:
- foaf_Person:
foaf_givenName: Bhiksha
foaf_name: Raj, Bhiksha
foaf_surname: Raj
- foaf_Person:
foaf_givenName: Kevin W.
foaf_name: Wilson, Kevin W.
foaf_surname: Wilson
- foaf_Person:
foaf_givenName: Alexander
foaf_name: Krueger, Alexander
foaf_surname: Krueger
- foaf_Person:
foaf_givenName: Reinhold
foaf_name: Haeb-Umbach, Reinhold
foaf_surname: Haeb-Umbach
foaf_workInfoHomepage: http://www.librecat.org/personId=242
dct_date: 2010^xs_gYear
dct_language: eng
dct_title: Ungrounded Independent Non-Negative Factor Analysis@
...