A deep clustering framework integrating pairwise constraints and a VMF mixture model

Autor:	He Ma, Weipeng Wu
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	generative deep clustering variational autoencoder von mises-fisher mixture model pairwise constraints kullback–leibler divergence Mathematics QA1-939 Applied mathematics. Quantitative methods T57-57.97
Zdroj:	Electronic Research Archive, Vol 32, Iss 6, Pp 3952-3972 (2024)
Druh dokumentu:	article
ISSN:	2688-1594
DOI:	10.3934/era.2024177?viewType=HTML
Popis:	We presented a novel deep generative clustering model called Variational Deep Embedding based on Pairwise constraints and the Von Mises-Fisher mixture model (VDEPV). VDEPV consists of fully connected neural networks capable of learning latent representations from raw data and accurately predicting cluster assignments. Under the assumption of a genuinely non-informative prior, VDEPV adopted a von Mises-Fisher mixture model to depict the hyperspherical interpretation of the data. We defined and established pairwise constraints by employing a random sample mining strategy and applying data augmentation techniques. These constraints enhanced the compactness of intra-cluster samples in the spherical embedding space while improving inter-cluster samples' separability. By minimizing Kullback-Leibler divergence, we formulated a clustering loss function based on pairwise constraints, which regularized the joint probability distribution of latent variables and cluster labels. Comparative experiments with other deep clustering methods demonstrated the excellent performance of VDEPV.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/74c78179db9c4c55b2c84c46f6a4efe9 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.