Conical Classification For Computationally Efficient One-Class Topic
  Determination
  As the Internet grows in size, so does the amount of text based information
that exists. For many application spaces it is paramount to isolate and
identify texts that relate to a particular topic. While one-class
classification would be ideal for such analysis, there is a relative lack of
research regarding efficient approaches with high predictive power. By noting
that the range of documents we wish to identify can be represented as positive
linear combinations of the Vector Space Model representing our text, we propose
Conical classification, an approach that allows us to identify if a document is
of a particular topic in a computationally efficient manner. We also propose
Normal Exclusion, a modified version of Bi-Normal Separation that makes it more
suitable within the one-class classification context. We show in our analysis
that our approach not only has higher predictive power on our datasets, but is
also faster to compute.
