Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spectral Learning of Binomial HMMs for DNA Methylation Data   We consider learning parameters of Binomial Hidden Markov Models, which may
be used to model DNA methylation data. The standard algorithm for the problem
is EM, which is computationally expensive for sequences of the scale of the
mammalian genome. Recently developed spectral algorithms can learn parameters
of latent variable models via tensor decomposition, and are highly efficient
for large data. However, these methods have only been applied to categorial
HMMs, and the main challenge is how to extend them to Binomial HMMs while still
retaining computational efficiency. We address this challenge by introducing a
new feature-map based approach that exploits specific properties of Binomial
HMMs. We provide theoretical performance guarantees for our algorithm and
evaluate it on real DNA methylation data.

33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an