  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.
