Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiple Correspondence Analysis & the Multilogit Bilinear Model   Multiple Correspondence Analysis (MCA) is a dimension reduction method which
plays a large role in the analysis of tables with categorical nominal variables
such as survey data. Though it is usually motivated and derived using geometric
considerations, in fact we prove that it amounts to a single proximal Newtown
step of a natural bilinear exponential family model for categorical data the
multinomial logit bilinear model. We compare and contrast the behavior of MCA
with that of the model on simulations and discuss new insights on the
properties of both exploratory multivariate methods and their cognate models.
One main conclusion is that we could recommend to approximate the multilogit
model parameters using MCA. Indeed, estimating the parameters of the model is
not a trivial task whereas MCA has the great advantage of being easily solved
by singular value decomposition and scalable to large data.

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The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
1
The categories of this paper are: cs
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess