Learning Mixtures of Multi-Output Regression Models by Correlation Clustering for Multi-View Data.
Year: 2017
Type: preprint
Abstract: In many datasets, different parts of the data may have their own patterns of correlation, a structure that can be modeled as a mixture of local linear correlation models. The task of finding these mixtures is known as correlation clustering. In this work, we propose a linear correlation clustering method for datasets whose features are pre-divided into two views. The method, called Canonical Least Squares (CLS) clustering, is inspired by multi-output regression and Canonical Correlation Analysis. CLS clusters can be interpreted as variations in the regression relationship between the two views. The method is useful for data mining and data interpretation. Its utility is demonstrated on a synthetic dataset and stock market dataset.
Source: arXiv (Cornell University)
Authors: Eric Lei, Kyle Miller, Artur Dubrawski
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2754657302