Local adaptive receptive field dimension selective self-organizing map for multi-view clustering

Images, text, web documents, videos, real-world data are very often high-dimensional. Many researchers or users may need to construct accurate predictive models for a variety of applications, especially those that involve clustering. Handling high dimensional data is a reality in processing task involving areas such as high-throughput genotyping platforms and human genetic clustering in bioinformatics, medical imaging and IMRT segmentation in medicine, market research, social network analysis, and anomaly detection. However, the performance of clustering algorithms usually decreases significantly when the sample dimension grows. Moreover, the big data can be acquired taking into consideration different views of them, characterizing the so-called multi-view clustering. In this paper, we use a subspace clustering approach, a time-varying self-organizing map, to deal with multi-view clustering. The method showed itself promising since it can handle real-world data characterized by high sparsity, high dimensionality nature, and different representations. A number of experiments with the proposed solution showed better performance than a number of other state-of-the-art models built specifically to deal with multi-view data.