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• W2401121137 abstract "This paper focuses on volume minimization (VolMin)-based structured matrix factorization (SMF), which factors a data matrix into a full-column rank basis and a coefficient matrix whose columns reside in the unit simplex. The VolMin criterion achieves this goal via finding a minimum-volume enclosing convex hull of the data. Recent works showed that VolMin guarantees the identifiability of the factor matrices under mild and realistic conditions, which suit many applications in signal processing and machine learning. However, the existing VolMin algorithms are sensitive to outliers or lack efficiency in dealing with volume-associated cost functions. In this work, we propose a new VolMin-based matrix factorization criterion and algorithm that take outliers into consideration. The proposed algorithm detects outliers and suppress them automatically, and it does so in an algorithmically very simple way. Simulations are used to showcase the effectiveness of the proposed algorithm." @default.
• W2401121137 created "2016-06-24" @default.
• W2401121137 creator A5002695199 @default.
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• W2401121137 date "2016-03-01" @default.
• W2401121137 modified "2023-10-14" @default.
• W2401121137 title "Robust volume minimization-based matrix factorization via alternating optimization" @default.
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• W2401121137 doi "https://doi.org/10.1109/icassp.2016.7472134" @default.
• W2401121137 hasPublicationYear "2016" @default.
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