Multi-view Clustering using Barycentric Coordinate Representation.

We consider clustering issues where the available attributes can be divided into various independent groups that frequently offer complimentary information. We concentrate on real-world applications in this paper where a single instance can be represented by a number of heterogeneous features. As was performed successfully in the prior work of clustering on a single view dataset by using barycentric coordinate(BC) representation, and also a recent KMeans-based multi-view clustering RMKMC which proposed that the weights of views can be auto-updated by introducing a hyperparameter gamma, we further propose a novel approach of multi-view clustering BCmvlearn by combining these two approaches to reduce complexity without sacrificing clustering quality. In addition, the vector form of the original dataset not being absolutely necessary due to the distance-based property of the BC representation, a variant application of multi-modal clustering is also achievable.