Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalizing Correspondence Analysis for Applications in Machine
  Learning   Correspondence analysis (CA) is a multivariate statistical tool used to
visualize and interpret data dependencies by finding maximally correlated
embeddings of pairs of random variables. CA has found applications in fields
ranging from epidemiology to social sciences; however, current methods do not
scale to large, high-dimensional datasets. In this paper, we provide a novel
interpretation of CA in terms of an information-theoretic quantity called the
principal inertia components. We show that estimating the principal inertia
components, which consists in solving a functional optimization problem over
the space of finite variance functions of two random variable, is equivalent to
performing CA. We then leverage this insight to design novel algorithms to
perform CA at an unprecedented scale. Particularly, we demonstrate how the
principal inertia components can be reliably approximated from data using deep
neural networks. Finally, we show how these maximally correlated embeddings of
pairs of random variables in CA further play a central role in several learning
problems including visualization of classification boundary and training
process, and underlying recent multi-view and multi-modal learning methods.

37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics