Search for unusual objects in the WISE Survey
  Automatic source detection and classification tools based on machine learning
(ML) algorithms are growing in popularity due to their efficiency when dealing
with large amounts of data simultaneously and their ability to work in
multidimensional parameter spaces. In this work, we present a new, automated
method of outlier selection based on support vector machine (SVM) algorithm
called one-class SVM (OCSVM), which uses the training data as one class to
construct a model of 'normality' in order to recognize novel points. We test
the performance of OCSVM algorithm on \textit{Wide-field Infrared Survey
Explorer (WISE)} data trained on the Sloan Digital Sky Survey (SDSS) sources.
Among others, we find $\sim 40,000$ sources with abnormal patterns which can be
associated with obscured and unobscured active galactic nuclei (AGN) source
candidates. We present the preliminary estimation of the clustering properties
of these objects and find that the unobscured AGN candidates are preferentially
found in less massive dark matter haloes ($M_{DMH}\sim10^{12.4}$) than the
obscured candidates ($M_{DMH}\sim 10^{13.2}$). This result contradicts the
unification theory of AGN sources and indicates that the obscured and
unobscured phases of AGN activity take place in different evolutionary paths
defined by different environments.
