{"status":"public","date_created":"2019-07-12T05:30:10Z","citation":{"apa":"Raj, B., Wilson, K. W., Krueger, A., & Haeb-Umbach, R. (2010). Ungrounded Independent Non-Negative Factor Analysis. In *Interspeech 2010*.","ama":"Raj B, Wilson KW, Krueger A, Haeb-Umbach R. Ungrounded Independent Non-Negative Factor Analysis. In: *Interspeech 2010*. ; 2010.","short":"B. Raj, K.W. Wilson, A. Krueger, R. Haeb-Umbach, in: Interspeech 2010, 2010.","bibtex":"@inproceedings{Raj_Wilson_Krueger_Haeb-Umbach_2010, title={Ungrounded Independent Non-Negative Factor Analysis}, booktitle={Interspeech 2010}, author={Raj, Bhiksha and Wilson, Kevin W. and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010} }","chicago":"Raj, Bhiksha, Kevin W. Wilson, Alexander Krueger, and Reinhold Haeb-Umbach. “Ungrounded Independent Non-Negative Factor Analysis.” In *Interspeech 2010*, 2010.","mla":"Raj, Bhiksha, et al. “Ungrounded Independent Non-Negative Factor Analysis.” *Interspeech 2010*, 2010.","ieee":"B. Raj, K. W. Wilson, A. Krueger, and R. Haeb-Umbach, “Ungrounded Independent Non-Negative Factor Analysis,” in *Interspeech 2010*, 2010."},"publication":"Interspeech 2010","oa":"1","user_id":"44006","author":[{"last_name":"Raj","first_name":"Bhiksha","full_name":"Raj, Bhiksha"},{"last_name":"Wilson","full_name":"Wilson, Kevin W.","first_name":"Kevin W."},{"full_name":"Krueger, Alexander","first_name":"Alexander","last_name":"Krueger"},{"last_name":"Haeb-Umbach","id":"242","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold"}],"type":"conference","_id":"11887","language":[{"iso":"eng"}],"year":"2010","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2010/RaWiKrHa10.pdf"}],"abstract":[{"lang":"eng","text":"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."}],"date_updated":"2022-01-06T06:51:11Z","title":"Ungrounded Independent Non-Negative Factor Analysis","department":[{"_id":"54"}]}