Probabilistic Factorization of Non-negative Data with Entropic Co-occurrence Constraints [chapter]

Paris Smaragdis, Madhusudana Shashanka, Bhiksha Raj, Gautham J. Mysore
<span title="">2009</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper we present a probabilistic algorithm which factorizes non-negative data. We employ entropic priors to additionally satisfy that user specified pairs of factors in this model will have their cross entropy maximized or minimized. These priors allow us to construct factorization algorithms that result in maximally statistically different factors, something that generic non-negative factorization algorithms cannot not explicitly guarantee. We further show how this approach can be used
more &raquo; ... to discover clusters of factors which allow a richer description of data while still effectively performing a low rank analysis.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-00599-2_42">doi:10.1007/978-3-642-00599-2_42</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mybpyx7e2vhpvepcy3nbo2jtrm">fatcat:mybpyx7e2vhpvepcy3nbo2jtrm</a> </span>
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