Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor decomposition for learning Gaussian mixtures from moments   In data processing and machine learning, an important challenge is to recover
and exploit models that can represent accurately the data. We consider the
problem of recovering Gaussian mixture models from datasets. We investigate
symmetric tensor decomposition methods for tackling this problem, where the
tensor is built from empirical moments of the data distribution. We consider
identifiable tensors, which have a unique decomposition, showing that moment
tensors built from spherical Gaussian mixtures have this property. We prove
that symmetric tensors with interpolation degree strictly less than half their
order are identifiable and we present an algorithm, based on simple linear
algebra operations, to compute their decomposition. Illustrative
experimentations show the impact of the tensor decomposition method for
recovering Gaussian mixtures, in comparison with other state-of-the-art
approaches.

36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex