Multi-view Low-rank Sparse Subspace Clustering

In many real-world machine learning problems the same data is comprised of several different representations or views. For example, same documents may be available in multiple languages [1] or different descriptors can be constructed from the same images [2]. Although each of these individual views may be sufficient to perform a learning task, integrating complementary information from different views can reduce the complexity of a given task [3]. Multi-view clustering seeks to partition data points based on multiple representations by assuming that the same cluster structure is shared across views. By combining information from different views, multi-view clustering algorithms attempt to achieve more accurate cluster assignments than one can get by simply concatenating features from different views. In practice, high-dimensional data often reside in a low-dimensional subspace. When all data points lie in a single subspace, the problem can be set as finding a basis of a subspace and a low-dimensional representation of data points. Depending on the constraints imposed on the lowdimensional representation, this problem can be solved using e.g.