Abstract
Multi-view clustering has attracted much attention recently. Among all clustering approaches, spectral ones have gained much popularity thanks to an elaborated and solid theoretical foundation. A major limitation of spectral clustering based methods is that these methods only provide a non-linear projection of the data, to which an additional step of clustering is required. This can degrade the quality of the final clustering due to various factors such as the initialization process or outliers. To overcome these challenges, this paper presents a constrained version of a recent method called Multiview Spectral Clustering via integrating Nonnegative Embedding and Spectral Embedding. Besides retaining the advantages of this method, our proposed model integrates two types of constraints: (i) a consistent smoothness of the nonnegative embedding over all views and (ii) an orthogonality constraint over the columns of the nonnegative embedding. Experimental results on several real datasets show the superiority of the proposed approach.
Sally El Hajjar
Former PhD Student
Former PhD student at CVPD, now Senior Research Fellow at The New School (NYC), working on climate risk prediction using deep learning.
Fadi Dornaika
Ikerbasque Research Professor
Ikerbasque Research Professor with expertise in computer vision, machine learning, and pattern recognition.