Attentive Multi-View Deep Subspace Clustering Net
- URL: http://arxiv.org/abs/2112.12506v1
- Date: Thu, 23 Dec 2021 12:57:26 GMT
- Title: Attentive Multi-View Deep Subspace Clustering Net
- Authors: Run-kun Lu, Jian-wei Liu, Xin Zuo
- Abstract summary: We propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN)
Our proposed method seeks to find a joint latent representation that explicitly considers both consensus and view-specific information.
The experimental results on seven real-world data sets have demonstrated the effectiveness of our proposed algorithm against some state-of-the-art subspace learning approaches.
- Score: 4.3386084277869505
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets
(AMVDSN), which deeply explores underlying consistent and view-specific
information from multiple views and fuse them by considering each view's
dynamic contribution obtained by attention mechanism. Unlike most multi-view
subspace learning methods that they directly reconstruct data points on raw
data or only consider consistency or complementarity when learning
representation in deep or shallow space, our proposed method seeks to find a
joint latent representation that explicitly considers both consensus and
view-specific information among multiple views, and then performs subspace
clustering on learned joint latent representation.Besides, different views
contribute differently to representation learning, we therefore introduce
attention mechanism to derive dynamic weight for each view, which performs much
better than previous fusion methods in the field of multi-view subspace
clustering. The proposed algorithm is intuitive and can be easily optimized
just by using Stochastic Gradient Descent (SGD) because of the neural network
framework, which also provides strong non-linear characterization capability
compared with traditional subspace clustering approaches. The experimental
results on seven real-world data sets have demonstrated the effectiveness of
our proposed algorithm against some state-of-the-art subspace learning
approaches.
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