Constrained NMF-Based Multi-View Clustering on Unmapped Data

We use the disagreement between the Multi-view clustering gains increasing attention in the past views to guide the factorization of the matrices. The overall decade (Bickel and Scheffer 2004) (Kumar and III 2011) objective of our algorithm is to minimize the loss function of (Kumar, Rai, and III 2011) (Liu et al. 2013) (Blaschko and NMF in each view as well as the disagreement between each Lampert 2008) (Chaudhuri et al. 2009) (Tzortzis and Likas pair of views. Experimental results show that, with a small 2012). Most existing multi-view clustering algorithms require number of constraints, the proposed CMVNMF (Constrained that the data is completely mapped, i.e., every object Multi-View clustering based on NMF) algorithm gets good has representations in all the views, representations from different performance on unmapped data, and outperforms existing views representing a same object are exactly known, algorithms on partially mapped data and completely mapped and the representations of the same object have the same data.