Abstract
Many real-life datasets exhibit structure in the form of physically meaningful clusters - e.g., news documents can be categorized as sports, politics, entertainment, and so on. Taking these clusters into account together with low-rank structure may yield parsimonious matrix and tensor factorization models and more powerful data analytics. Prior works made use of data-domain similarity to improve nonnegative matrix factorization. Here we are instead interested in joint low-rank factorization and latent-domain clustering; that is, in clustering the latent reduced-dimension representations of the observed entities. A unified algorithmic framework that can deal with both matrix and tensor factorization and latent clustering is proposed. Numerical results obtained from synthetic and real document data show that the proposed approach can significantly improve factor analysis and clustering accuracy.
Original language | English (US) |
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Title of host publication | 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 173-176 |
Number of pages | 4 |
ISBN (Electronic) | 9781479919635 |
DOIs | |
State | Published - 2015 |
Event | 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 - Cancun, Mexico Duration: Dec 13 2015 → Dec 16 2015 |
Publication series
Name | 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 |
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Other
Other | 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 |
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Country/Territory | Mexico |
City | Cancun |
Period | 12/13/15 → 12/16/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.