Abstract
We investigate an alternative way of combining classification and clustering techniques for sparse binary data in order to reduce the amount of training samples required. Initializing EM from the available labels also reduces the algorithms' known dependency on the initialization, which is more evident in the case of sparse data. In addition, the two-valued Poisson class-model is proposed in this paper as a sparse variant of the usual binomial assumption. Our method can be seen as a fusion between generalized logistic regression and parametric mixture modeling. Comparative simulation results on subsets of the 20 Newsgroups' binary coded text corpora and binary handwritten digits data demonstrate the potential usefulness of the suggested method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
