Binary Multi-View Clustering

By Zheng Zhang, Li Liu, Fumin Shen, Heng Tao Shen, Ling Shao. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019

542 citations recorded by the source corpus.

Clustering is a long-standing important research problem, however, remains challenging when handling large-scale image data from diverse sources. In this paper, we present a novel Binary Multi-View Clustering (BMVC) framework, which can dexterously manipulate multi-view image data and easily scale to large data. To achieve this goal, we formulate BMVC by two key components: compact collaborative discrete representation learning and binary clustering structure learning, in a joint learning framework. Specifically, BMVC collaboratively encodes the multi-view image descriptors into a compact common binary code space by considering their complementary information; the collaborative binary representations are meanwhile clustered by a binary matrix factorization model, such that the cluster structures are optimized in the Hamming space by pure, extremely fast bit-operations. For efficiency, the code balance constraints are imposed on both binary data representations and cluster centroids. Finally, the resulting optimization problem is solved by an alternating optimization scheme with guaranteed fast convergence. Extensive experiments on four large-scale multi-view image datasets demonstrate that the proposed method enjoys the significant reduction in both computation and memory footprint, while observing superior (in most cases) or very competitive performance, in comparison with state-of-the-art clustering methods.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/eb22f2ced9139a122103a236eac564b1525c5d05

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Huan Fu
3. Xiaochun Cao
4. Yuan Xie
5. Changqing Zhang
6. Qinghua Hu
7. Joey Tianyi Zhou
8. Hong Liu

In Media Technology

1. Yuan Xie
2. Hong Liu
3. Joey Tianyi Zhou
4. Dacheng Tao
5. Peng Hu
6. Xi Peng
7. Qinghua Hu
8. Changqing Zhang

Citations in the corpus

• Robust Multi-View Clustering With Incomplete Information (2022, methodology): “We compare SURE with 10 multi-view clustering baselines including CCA [12], KCCA [11], DCCA [1], DCCAE [2], BMVC [14], AE 2 -Nets [4], PMVC [51], UEAF [58], DAIMC [16] and EERIMC [17].”
• COMIC: Multi-view Clustering Without Parameter Selection (2019, methodology): “We compare our methods with nine MvC approaches, namely, the vanilla k-means, the normalized spectral clustering (SC) (Ng et al., 2002), low rank representation (LRR) (Liu et al., 2016), diversity-induced multi-view subspace clustering (DiMSC) (Cao et al., 2015), latent multi-view subspace clusterin”
• Generalized Latent Multi-View Subspace Clustering (2020, methodology): “In the future, sampling technique and binary representations [65] will be considered to accelerate the clustering speed.”
• Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, background): “data points into their respective groups without any label information [13]–[16].”
• Dual Contrastive Prediction for Incomplete Multi-View Representation Learning (2022, background): “(3) BMVC (Binary Multi-view Clustering) [60] jointly learns the collaborative discrete representations and binary cluster structures.”
• COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (2021, background): “By ignoring the constants derived from the Gaussian distribution, maximizing For a given bi-view dataset, we further have It should be pointed out that the above loss may lead to trivial solutions without the within-view reconstruction loss, i.e ., Z 1 and Z 2 are equivalent to the same constant.”