Agglomerative Neural Networks for Multi-view Clustering
This work addresses multi-view clustering for data analysis, presenting an incremental improvement by introducing agglomerative analysis to better handle inter-view relationships.
The paper tackles the problem of multi-view clustering by proposing an agglomerative neural network (ANN) to approximate an optimal consensus view, avoiding pairwise discrepancy minimization and dedicated postprocessing steps. Evaluations on four datasets show promising view-consensus analysis ability.
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, the pairwise comparison cannot portray the inter-view relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present Agglomerative Neural Network (ANN) based on Constrained Laplacian Rank to cluster multi-view data directly while avoiding a dedicated postprocessing step (e.g., using K-means). We further extend ANN with learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multi-view clustering approaches on four popular datasets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures and extensibility in our case study and explain its robustness and effectiveness of data-driven modifications.