Abstract
Clustering is a compression task which consists in grouping similar objects into clusters.
In real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algorithm: this framework is called multi-view clustering and can benefit from algorithms capable of exchanging information between the different views.
In this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering and collaborative clustering applications.
Using this approach, we propose a new algorithm based on solid theoretical basis, and test it on several real and artificial data sets.
Keywords
- Machine Learning: Multi-instance;Multi-label;Multi-view learning
- Machine Learning: Clustering
