Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Maximum likelihood estimation in constrained parameter spaces for
  mixtures of factor analyzers   Mixtures of factor analyzers are becoming more and more popular in the area
of model based clustering of high-dimensional data. According to the likelihood
approach in data modeling, it is well known that the unconstrained
log-likelihood function may present spurious maxima and singularities and this
is due to specific patterns of the estimated covariance structure, when their
determinant approaches 0. To reduce such drawbacks, in this paper we introduce
a procedure for the parameter estimation of mixtures of factor analyzers, which
maximizes the likelihood function in a constrained parameter space. We then
analyze and measure its performance, compared to the usual non-constrained
approach, via some simulations and applications to real data sets.

31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an