Ungrounded Independent Non-Negative Factor Analysis
Raj, Bhiksha
Wilson, Kevin W.
Krueger, Alexander
Haeb-Umbach, Reinhold
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.
2010
info:eu-repo/semantics/conferenceObject
doc-type:conferenceObject
text
http://purl.org/coar/resource_type/c_5794
https://ris.uni-paderborn.de/record/11887
Raj B, Wilson KW, Krueger A, Haeb-Umbach R. Ungrounded Independent Non-Negative Factor Analysis. In: <i>Interspeech 2010</i>. ; 2010.
eng
info:eu-repo/semantics/openAccess