A Bidirectional Adaptive Bandwidth Mean Shift Strategy for Clustering
This is an incremental improvement for clustering tasks in machine learning.
The paper tackles the problem of bandwidth selection in the mean shift clustering algorithm by proposing a bidirectional adaptive bandwidth strategy that improves the ability to escape local maxima, with experiments showing its effectiveness compared to contemporary methods.
The bandwidth of a kernel function is a crucial parameter in the mean shift algorithm. This paper proposes a novel adaptive bandwidth strategy which contains three main contributions. (1) The differences among different adaptive bandwidth are analyzed. (2) A new mean shift vector based on bidirectional adaptive bandwidth is defined, which combines the advantages of different adaptive bandwidth strategies. (3) A bidirectional adaptive bandwidth mean shift (BAMS) strategy is proposed to improve the ability to escape from the local maximum density. Compared with contemporary adaptive bandwidth mean shift strategies, experiments demonstrate the effectiveness of the proposed strategy.