Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonstationary Distance Metric Learning   Recent work in distance metric learning has focused on learning
transformations of data that best align with provided sets of pairwise
similarity and dissimilarity constraints. The learned transformations lead to
improved retrieval, classification, and clustering algorithms due to the better
adapted distance or similarity measures. Here, we introduce the problem of
learning these transformations when the underlying constraint generation
process is nonstationary. This nonstationarity can be due to changes in either
the ground-truth clustering used to generate constraints or changes to the
feature subspaces in which the class structure is apparent. We propose and
evaluate COMID-SADL, an adaptive, online approach for learning and tracking
optimal metrics as they change over time that is highly robust to a variety of
nonstationary behaviors in the changing metric. We demonstrate COMID-SADL on
both real and synthetic data sets and show significant performance improvements
relative to previously proposed batch and online distance metric learning
algorithms.

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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys