Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Binary Independent Component Analysis via Non-stationarity   We consider independent component analysis of binary data. While fundamental
in practice, this case has been much less developed than ICA for continuous
data. We start by assuming a linear mixing model in a continuous-valued latent
space, followed by a binary observation model. Importantly, we assume that the
sources are non-stationary; this is necessary since any non-Gaussianity would
essentially be destroyed by the binarization. Interestingly, the model allows
for closed-form likelihood by employing the cumulative distribution function of
the multivariate Gaussian distribution. In stark contrast to the
continuous-valued case, we prove non-identifiability of the model with few
observed variables; our empirical results imply identifiability when the number
of observed variables is higher. We present a practical method for binary ICA
that uses only pairwise marginals, which are faster to compute than the full
multivariate likelihood.

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The categories of this paper are: atom-ph
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The categories of this paper are: math
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The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas