Feb 8, 2021 · Self organizing maps (SOMs) are neural networks designed to be in an unsupervised way to create connections, learned through a modified ...
SOMs require three functions in order to construct the final cognitive map, namely the distance metrics of V and C and the weight function. As its name suggests ...
Nov 21, 2024 · Self organizing maps (SOMs) are neural networks designed to be in an unsupervised way to create connections, learned through a modified ...
A multilinear distance metric for the input vector space which adds flexibility in two ways: first, clustering can be extended to higher order data such as ...
Jan 4, 2021 · Self organizing maps (SOMs) are neural networks designed to be in an unsupervised way to create connections, learned through a modified ...
TL;DR: A multilinear distance metric for the input vector space which adds flexibility in two ways: first, clustering can be extended to higher order data ...
Sioutas, "On Tensor Distances For Self Organizing Maps: Clustering Cognitive Tasks", 31st International Conference on Database and Expert Systems ...
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In this paper, we present a unified framework for tensor based dimensionality reduction including a new tensor distance (TD) metric and a novel multilinear ...
Self-organizing maps have a bearing on traditional vector quantization. A ... On Tensor Distances for Self Organizing Maps: Clustering Cognitive Tasks ...