Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Flexible Framework for Anomaly Detection via Dimensionality Reduction   Anomaly detection is challenging, especially for large datasets in high
dimensions. Here we explore a general anomaly detection framework based on
dimensionality reduction and unsupervised clustering. We release DRAMA, a
general python package that implements the general framework with a wide range
of built-in options. We test DRAMA on a wide variety of simulated and real
datasets, in up to 3000 dimensions, and find it robust and highly competitive
with commonly-used anomaly detection algorithms, especially in high dimensions.
The flexibility of the DRAMA framework allows for significant optimization once
some examples of anomalies are available, making it ideal for online anomaly
detection, active learning and highly unbalanced datasets.

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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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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: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: eess