Lopez-Rubio E. Improving the quality of self-organizing maps by self-intersection avoidance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2013;
24:1253-1265. [PMID:
24808565 DOI:
10.1109/tnnls.2013.2254127]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The quality of self-organizing maps is always a key issue to practitioners. Smooth maps convey information about input data sets in a clear manner. Here a method is presented to modify the learning algorithm of self-organizing maps to reduce the number of topology errors, hence the obtained map has better quality at the expense of increased quantization error. It is based on avoiding maps that self-intersect or nearly so, as these states are related to low quality. Our approach is tested with synthetic data and real data from visualization, pattern recognition and computer vision applications, with satisfactory results.
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