We propose a knowledge-informed variant for learning covariance-dependent data representations using unsupervised vector quantization. In particular, we consider linear data mappings included in vectorquantization mo...
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ISBN:
(纸本)9783031671586;9783031671593
We propose a knowledge-informed variant for learning covariance-dependent data representations using unsupervised vector quantization. In particular, we consider linear data mappings included in vectorquantization models, such as c-means++, neural gas, or self-organizing maps, to achieve representations in a lower-dimensional data space. To this end, we show how additional data knowledge can be integrated into the models. The additional data structure information is used to generate an appropriate data mapping depending on the corresponding structured data covariances.
In this paper we present a necessary and sufficient condition for global optimality of unsupervised Learning vectorquantization (LVQ) in kernel space. In particular, we generalize the results presented for expansive ...
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In this paper we present a necessary and sufficient condition for global optimality of unsupervised Learning vectorquantization (LVQ) in kernel space. In particular, we generalize the results presented for expansive and competitive learning for vectorquantization in Euclidean space, to the general case of a kernel-based distance metric. Based on this result, we present a novel kernel LVQ algorithm with an update rule consisting of two terms: the former regulates the force of attraction between the synaptic weight vectors and the inputs: the latter, regulates the repulsion between the weights and the center of gravity of the dataset. We show how this algorithm pursues global optimality of the quantization error by means of the repulsion mechanism. Simulation results are provided to show the performance of the model on common image quantization tasks: in particular, the algorithm is shown to have a superior performance with respect to recently published quantization models such as Enhanced LBG [Patane, G., Russo, M., 2001. The enhanced LBG algorithm. Neural Networks 14 (9). 1219-1237] and Adaptive Incremental LBG [Shen, F., Hasegawa, O., 2006. An adaptive incremental LBG for vectorquantization. Neural Networks 19 (5), 694-704]. (C) 2009 Elsevier B.V. All rights reserved.
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