Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and ; Proceedings of the 1 Thomas Villmann,

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,New Cloth Unto an?Old Garment: SOM for?Regeneration Learning,urrent cross-modal representation and regeneration learning rely on supervised deep learning models, this paper aims to revisit the adequacy of unsupervised models in this field. In this regard, we propose a new unsupervised approach that utilizes the SOM as a heteroassociative memory model to learn

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,Unsupervised Learning-Based Data Collection Planning with?Dubins Vehicle and?Constrained Data Retri to retrieve required data from the site. The planning task is to find a cost-efficient data collection plan to retrieve data from all the stations. For a fixed-wing aerial vehicle flying with a constant forward velocity, the problem is to determine the shortest feasible path that visits every sensi

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,Sparse Clustering with?,-Means - Which Penalties and?for?Which Data?,ised learning and particularly in clustering. The presence of uninformative features may bias significantly the results of distance-based methods such as .-means for instance. For tackling this issue, different versions of sparse .-means have been introduced, building on the idea of adding some pena

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,Is t-SNE Becoming the?New Self-organizing Map? Similarities and?Differences,that are defined in a low-dimensional space, they can run on big data sets and are mostly immune to the curse of dimensionality in the data space..SOMs are used mainly for dimensionality reduction and marginally for clustering; however, SOMs also suffer from some shortcomings..Vector quantization ma

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