Kernel neural gas algorithms with application to cluster analysis

We present a kernel neural gas (KNG) algorithm, to generalize the original neural gas (NG) algorithm into a higher dimensional feature space. The proposed KNG algorithm can successfully tackle nonlinearly structured datasets. Compared with several existing kernel clustering algorithms, the KNG can be insensitive to initializations, due to the employment of the sequential learning strategy and the neighborhood cooperation scheme. Further, a distortion sensitive KNG (DSKNG) algorithm is proposed to tackle the imbalanced clustering problem. Experimental results show that our KNG algorithm can successfully deal with nonlinearly structured datasets and multi-modal datasets, while the imbalanced clusters are detected bv the DSKNG.