One-Step Late Fusion Multi-view Clustering with Compressed Subspace (2401.01558v3)
Abstract: Late fusion multi-view clustering (LFMVC) has become a rapidly growing class of methods in the multi-view clustering (MVC) field, owing to its excellent computational speed and clustering performance. One bottleneck faced by existing late fusion methods is that they are usually aligned to the average kernel function, which makes the clustering performance highly dependent on the quality of datasets. Another problem is that they require subsequent k-means clustering after obtaining the consensus partition matrix to get the final discrete labels, and the resulting separation of the label learning and cluster structure optimization processes limits the integrity of these models. To address the above issues, we propose an integrated framework named One-Step Late Fusion Multi-view Clustering with Compressed Subspace (OS-LFMVC-CS). Specifically, we use the consensus subspace to align the partition matrix while optimizing the partition fusion, and utilize the fused partition matrix to guide the learning of discrete labels. A six-step iterative optimization approach with verified convergence is proposed. Sufficient experiments on multiple datasets validate the effectiveness and efficiency of our proposed method.
- “Kernel k-means: spectral clustering and normalized cuts,” in KDD, 2004.
- “Approximate kernel k-means: Solution to large scale kernel clustering,” in ACM SIGKDD, 2011.
- “Robust multiple kernel k-means using l21-norm,” in IJCAI, 2015.
- Xinwang Liu, “Simplemkkm: Simple multiple kernel k-means,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
- “Multiple kernel k-means clustering with matrix-induced regularization.,” in AAAI, 2016.
- “Multiple kernel clustering with kernel k-means coupled graph tensor learning,” in AAAI, 2021.
- “Scalable multiple kernel k-means clustering,” in CIKM, 2022.
- “A cluster-weighted kernel k-means method for multi-view clustering,” in AAAI, 2020.
- “Multiple kernel k-means clustering with simultaneous spectral rotation,” in ICASSP, 2022.
- “Efficient multiple kernel k-means clustering with late fusion,” IEEE Access, 2019.
- “Late fusion multiple kernel clustering with proxy graph refinement,” TNNLS, 2021.
- “Optimal neighborhood multiple kernel clustering with adaptive local kernels,” TKDE, 2022.
- “Classification of heterogeneous mining areas based on rescapsnet and gaofen-5 imagery,” Remote Sensing, 2022.
- “A survey of deep graph clustering: Taxonomy, challenge, and application,” arXiv preprint arXiv:2211.12875, 2022.
- “Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks,” arXiv preprint arXiv:2312.06068, 2023.
- “Deep temporal graph clustering,” arXiv preprint arXiv:2305.10738, 2023.
- “Spectral rotation versus k-means in spectral clustering.,” in AAAI, 2013.
- “Spectral clustering by joint spectral embedding and spectral rotation.,” IEEE Trans. Cybern., 2020.
- “Discrete multi-kernel k-means with diverse and optimal kernel learning,” in ICASSP, 2022.
- “Scalable normalized cut with improved spectral rotation.,” in IJCAI, 2017.
- “Extended grassmann kernels for subspace-based learning,” in NeuralIPS 21, 2008.
- “Optimal mean robust principal component analysis,” in ICML, 2014.
- “Late fusion incomplete multi-view clustering.,” TPAMI, 2019.
- “Anchor-based multiview subspace clustering with diversity regularization,” IEEE MultiMedia, 2020.
- “A reflective newton method for minimizing a quadratic function subject to bounds on some of the variables,” SIAM J. Optim., 1996.
- “Multiple kernel fuzzy clustering,” IEEE Trans. Fuzzy Syst., 2012.
- “Fusion multiple kernel k-means,” in AAAI, 2022.
- “Robust multi-view spectral clustering via low-rank and sparse decomposition,” in AAAI, 2014.
- “Flexible multi-view representation learning for subspace clustering,” in IJCAI, 2019.
- “Multiview subspace clustering via low-rank symmetric affinity graph,” TNNLS, 2023.