Metric learning and dimensionality reduction in clustering
R.V. Isachenko, Alexandr Katrutsa · Machine Learning and Data Analysis · 2016
This paper investigates incorporation of metric learning approach in clustering problem.Distance metric is a key issue in many machine learning algorithms, especially in unsupervised learning where distance between objects is the only known information.The metric learning procedure modifies distances between objects to make objects from the same cluster closer and from the different clusters more distant.In this paper, Mahalanobis distance is used as a distance between objects.The goal of the paper is to learn Mahalanobis metric by optimizing the covariance matrix of objects according to their cluster labels.In this case, metric learning procedure is formulated as optimization problem.For clustering, k-means were used as baseline algorithm and Adaptive Metric Learning (AML) algorithm.To solve the problem, AML algorithm uses iterative EM (expectation-maximization) procedure to find the optimum.To compare these algorithms, the computational experiment was carried out in MatLab on synthetic data and real data from UCI repository and conclusions about performance of these algorithms have been made.