Soft dimensionality reduction for reinforcement data clustering

The standard Euclidean distance considers equal contributions for all features of each data sample pair when computing the similarity matrix, while different features of real-world datasets have different importance. This paper proposes a new clustering method based on reinforcement learning and soft feature selection with three innovative ideas. First, a novel distance metric based on the importance of features is introduced which additionally can disappear irrelevant features approximately. Second, a new soft weighting mechanism is defined based on this distance to determine the effect of the neighborhood probability in the similarity matrix. Since the training data consists of noisy and redundant features, a sparsity regularization term is applied to solve this problem and emphasizes feature selection. Third, after these dimensionality reduction steps, a new clustering method is developed according to reinforcement learning, which considers the obtained low-dimensional data points as the states of the learning agents. It also uses different actions until convergence to transfer the worst points with the most scattering from one cluster to another one, to produce coherent clusters as well as make a balance between them. The proposed method is able to present high within-cluster consistencies. The experimental results on several real-world datasets show good performance and efficiency of the proposed method. Statistical analysis, parameter sensitivity analysis, and time complexity analysis, all confirm the appropriateness of the results obtained.