1,370 research outputs found
Deep Clustering: A Comprehensive Survey
Cluster analysis plays an indispensable role in machine learning and data
mining. Learning a good data representation is crucial for clustering
algorithms. Recently, deep clustering, which can learn clustering-friendly
representations using deep neural networks, has been broadly applied in a wide
range of clustering tasks. Existing surveys for deep clustering mainly focus on
the single-view fields and the network architectures, ignoring the complex
application scenarios of clustering. To address this issue, in this paper we
provide a comprehensive survey for deep clustering in views of data sources.
With different data sources and initial conditions, we systematically
distinguish the clustering methods in terms of methodology, prior knowledge,
and architecture. Concretely, deep clustering methods are introduced according
to four categories, i.e., traditional single-view deep clustering,
semi-supervised deep clustering, deep multi-view clustering, and deep transfer
clustering. Finally, we discuss the open challenges and potential future
opportunities in different fields of deep clustering
Reconsidering Representation Alignment for Multi-view Clustering
Aligning distributions of view representations is a core component of today's
state of the art models for deep multi-view clustering. However, we identify
several drawbacks with na\"ively aligning representation distributions. We
demonstrate that these drawbacks both lead to less separable clusters in the
representation space, and inhibit the model's ability to prioritize views.
Based on these observations, we develop a simple baseline model for deep
multi-view clustering. Our baseline model avoids representation alignment
altogether, while performing similar to, or better than, the current state of
the art. We also expand our baseline model by adding a contrastive learning
component. This introduces a selective alignment procedure that preserves the
model's ability to prioritize views. Our experiments show that the contrastive
learning component enhances the baseline model, improving on the current state
of the art by a large margin on several datasets.Comment: To appear in CVPR 2021. Code available at
https://github.com/DanielTrosten/mv
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