DCSR: Deep clustering under similarity and reconstruction constraints

Clustering is a difficult but crucial task in pattern recognition and machine learning. Inherently, clustering methods are always subject to the uncertainty of similarities between samples. To weaken the impact of such uncertainty, we develop Deep Clustering with both Adaptive Siamese Loss (ASL) and...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 411; pp. 216 - 228
Main Authors Yu, Lei, Wang, Wei
Format Journal Article
LanguageEnglish
Published Elsevier B.V 21.10.2020
Subjects
Adaptive siamese loss
Clustering
Reconstruction loss
Clustering
Reconstruction loss
Adaptive siamese loss
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Summary:Clustering is a difficult but crucial task in pattern recognition and machine learning. Inherently, clustering methods are always subject to the uncertainty of similarities between samples. To weaken the impact of such uncertainty, we develop Deep Clustering with both Adaptive Siamese Loss (ASL) and ReConstruction Loss (RCL) to adaptively consider the similarities and stabilize the clustering process. Technically, ASL is focus on mapping the samples from the data space to the same representations or the orthogonal representations, and RCL provides a priori knowledge to stabilize the clustering process. Benefiting from such artful modelling, DCSR is in a position to endow deep networks to stably learn one-hot representations, yielding an end-to-end mechanism for clustering and representation learning. Extensive experiments demonstrate that our model achieves state-of-the-art performance on popular datasets, including image, audio, and text.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.06.013