Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation
摘要
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 vector quantization 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.