Reinforcement Learning based Evolutionary Metric Filtering for High Dimensional Problems
Bassel Ali, Koichi Moriyama, Masayuki Numao, Ken–ichi Fukui · 2020
Metric learning algorithms create distance metrics to capture the important relationships among features. These algorithms have been successful in low dimensional data. However, it is a challenge to handle high dimensional problems in real-world applications. This paper applies in high dimensional clustering problems a Reinforcement Learning (RL) based metric filtering approach in Evolutionary Distance Metric Learning (EDML), which relies on an evolutionary approach to optimize its distance metric. The proposed framework called High Dimensional Reinforced (HDR)-EDML has two novelties. The first is using a function approximation RL method called Least-Squares Policy Iteration (LSPI) to create a feature selection control strategy in high-dimensional input space to filter the metric. The second is adopting a two-way information exchange approach between LSPI and EDML Evolutionary Algorithm (EA) in metric learning. In the first way, LSPI will learn and send feedback that will affect the EDML metric creation. In the second way, the evolutionary feature prioritizing of EDML is utilized by LSPI in its learning process. These two novelties aim to reduce the number of selected features while maintaining the clustering performance. HDR-EDML is compared to conventional K-means, Information-Theoretic Metric Learning (ITML), normal EDML, and EDML with l1norm regularization for feature selection as baselines. Moreover, 3 different HDR-EDML approaches are examined to explore different ways of information exchange. Results show a significant decrease in the number of features while maintaining accuracy as well as reduced computational time and memory compared to another RL-based filtering method.