… a good reduced Gaussian mixture becomes equal to finding good cluster centers using an
… proposed algorithm for Gaussian mixture reduction yields results with a quality on the same …

… Abstract—We review the literature and look at two of the best algorithms for Gaussian
mixture reduction, the GMRC (Gaussian Mixture Reduction via Clustering) and the COWA (…

… the shape of the components, in this work, the center of the clusters is found by seeking a
Gaussian density, which minimizes the average distance for cluster i defined as follows: …

… in objective reduction. More specifically, we use the Gaussian mixture model clustering to …
different subsets and perform objective reduction on each subset. Both an offline objective …

… contain clusters embedded … clustering and finding each cluster’s intrinsic subspace
simultaneously, in this paper, we propose a regularized Gaussian mixture model (GMM) for clustering…

… clustering via a Gaussianmixture variational autoencoder (VAE) with Graph embedding. To
facilitate clustering, we apply Gaussian mix… -based approaches for clustering. To combine …

… Specifically, we introduce a regularized probabilistic model based on manifold structure
for data clustering, called Laplacian regularized Gaussian Mixture Model (LapGMM). …

… Gaussian mixture clustering without class labels. This paper, therefore, proposes a new feature
selection method, through … our recently proposed Gaussian mixture clustering approach, …

… The number of alternative cluster centres and mixture models was determined according
to … In this study, appropriate Gaussian mixture models were determined with the help of "mixture …

… , the novel Gaussian mixture reduction algorithm introduced in this paper avoids to directly
reduce the original Gaussian mixture. Instead, an approximate mixture is generated from …