Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
Convex Nonnegative Matrix Factorization with Rank-1 Update for Clustering
verfasst von : Rafał Zdunek
Erschienen in: Artificial Intelligence and Soft Computing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In convex nonnegative matrix factorization, the feature vectors are modeled by convex combinations of observation vectors. In the paper, we propose to express the factorization model in terms of the sum of rank-1 matrices. Then the sparse factors can be easily estimated by applying the concept of the Hierarchical Alternating Least Squares (HALS) algorithm which is still regarded as one of the most effective algorithms for solving many nonnegative matrix factorization problems. The proposed algorithm has been applied to find partially overlapping clusters in various datasets, including textual documents. The experiments demonstrate the high performance of the proposed approach.