Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Efficient Model Selection for Gaussian Mixture Model in a Bayesian
  Framework   In order to cluster or partition data, we often use
Expectation-and-Maximization (EM) or Variational approximation with a Gaussian
Mixture Model (GMM), which is a parametric probability density function
represented as a weighted sum of $\hat{K}$ Gaussian component densities.
However, model selection to find underlying $\hat{K}$ is one of the key
concerns in GMM clustering, since we can obtain the desired clusters only when
$\hat{K}$ is known. In this paper, we propose a new model selection algorithm
to explore $\hat{K}$ in a Bayesian framework. The proposed algorithm builds the
density of the model order which any information criterions such as AIC and BIC
basically fail to reconstruct. In addition, this algorithm reconstructs the
density quickly as compared to the time-consuming Monte Carlo simulation.

7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph