Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Similarity measure for sparse time course data based on Gaussian
  processes   We propose a similarity measure for sparsely sampled time course data in the
form of a log-likelihood ratio of Gaussian processes (GP). The proposed GP
similarity is similar to a Bayes factor and provides enhanced robustness to
noise in sparse time series, such as those found in various biological
settings, e.g., gene transcriptomics. We show that the GP measure is equivalent
to the Euclidean distance when the noise variance in the GP is negligible
compared to the noise variance of the signal. Our numerical experiments on both
synthetic and real data show improved performance of the GP similarity when
used in conjunction with two distance-based clustering methods.

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The categories of this paper are: physics
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
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The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg