Title of Paper:A Multimodal Clustering Framework With Cross Reconstruction Autoencoders

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Date of Publication:2021-01-10

Journal:IEEE ACCESS

Volume:8

Page Number:218433-218443

ISSN No.:2169-3536

Key Words:Feature extraction; Clustering algorithms; Neural networks; Data mining; Image reconstruction; Decoding; Correlation; Multimodal clustering; unsupervised deep learning; early fusion

Abstract:Multimodal clustering algorithms partitions a multimodal dataset into disjoint clusters. Common feature extraction is a key part in multimodal clustering algorithms. Recently, deep neural networks shows high performance on latent feature extraction. However, existing works did not completely explore the cross-model distribution similarity utilizing deep neural networks. We present a deep multimodal clustering framework with cross reconstruction. Feature extraction apply global cross reconstruction and local cross reconstruction respectively to enforce early fusion among different modalities. Analysis shows that the both cross reconstruction networks reduces the Wasserstein distance of latent feature distributions, which indicates that the proposed framework ensures the distribution similarity of common latent features. Experimental results on benchmark datasets demonstrate superiority beyond existing works.