Gradient-Based Training of Gaussian Mixture Models for High-Dimensional Streaming Data
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DOI: 10.1007/s11063-021-10599-3
Abstract: We present an approach for efficiently training Gaussian Mixture Model (GMM) by Stochastic Gradient Descent (SGD) with non-stationary, high-dimensional streaming data. Our training scheme does not require data-driven parameter initialization (e.g., k-means) and can thus… read more here.
Keywords: high dimensional; streaming data; gaussian mixture; training gaussian ... See more keywords