Joint Multi-View Collaborative Clustering
Yasser Khalafaoui Basarab Matei Nistor Grozavu Martino Lovisetto
Data is increasingly being collected from multiple sources and described by
multiple views. These multi-view data provide richer information than
traditional single-view data. Fusing the former for specific tasks is an
essential component of multi-view clustering. Since the goal of multi-view
clustering algorithms is to discover the common latent structure shared by
multiple views, the majority of proposed solutions overlook the advantages of
incorporating knowledge derived from horizontal collaboration between
multi-view data and the final consensus. To fill this gap, we propose the Joint
Multi-View Collaborative Clustering (JMVCC) solution, which involves the
generation of basic partitions using Non-negative Matrix Factorization (NMF)
and the horizontal collaboration principle, followed by the fusion of these
local partitions using ensemble clustering. Furthermore, we propose a weighting
method to reduce the risk of negative collaboration (i.e., views with low
quality) during the generation and fusion of local partitions. The experimental
results, which were obtained using a variety of data sets, demonstrate that
JMVCC outperforms other multi-view clustering algorithms and is robust to noisy
views.