Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Residual Component Analysis: Generalising PCA for more flexible
  inference in linear-Gaussian models   Probabilistic principal component analysis (PPCA) seeks a low dimensional
representation of a data set in the presence of independent spherical Gaussian
noise. The maximum likelihood solution for the model is an eigenvalue problem
on the sample covariance matrix. In this paper we consider the situation where
the data variance is already partially explained by other actors, for example
sparse conditional dependencies between the covariates, or temporal
correlations leaving some residual variance. We decompose the residual variance
into its components through a generalised eigenvalue problem, which we call
residual component analysis (RCA). We explore a range of new algorithms that
arise from the framework, including one that factorises the covariance of a
Gaussian density into a low-rank and a sparse-inverse component. We illustrate
the ideas on the recovery of a protein-signaling network, a gene expression
time-series data set and the recovery of the human skeleton from motion capture
3-D cloud data.

26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat