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Vector batch SOM algorithms for multi-view dissimilarity data
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DOI：10.1016/j.knosys.2022.109994.png)
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Multi-view data has become fairly important since large amounts of information are constantly generated from different sources. So far, most multi-view research on unsupervised learning has focused on clustering algorithms suitable for analyzing vector data. Instead, our study presents two batch SOM algorithms for multi-view dissimilarity data, namely a batch SOM with each cluster representative as a vector of a subset of objects (vector of set-medoids) and another batch SOM for multi-view dissimilarity data with each cluster representative as a matrix whose elements are the weights of its objects regarding the dissimilarity matrices (weighted medoids). These algorithms are suitable for clustering and visualizing multi-view dissimilarity data. In addition, they provide the relevance weights on the importance of each dissimilarity matrix (view) concerning each cluster related to the neurons in the map. Furthermore, the weight can be computed both locally, for each cluster, or globally, for the whole partition. Experiments with 12 datasets showed the importance of multi-view algorithms with respect to single-view algorithms in some contexts. Among the multi-view algorithms, the results showed the need to consider the relevance weights of the dissimilarity matrices. Moreover, an application study on the FOREST TYPE dataset found that some of the proposed methods provide better-quality maps.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Self-Organizing Maps
Batch SOM
Multi-view dissimilarity data
Relevance weights
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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