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Literary linguistics: Open Access research in English language

Strathprints makes available Open Access scholarly outputs by English Studies at Strathclyde. Particular research specialisms include literary linguistics, the study of literary texts using techniques drawn from linguistics and cognitive science.

The team also demonstrates research expertise in Renaissance studies, researching Renaissance literature, the history of ideas and language and cultural history. English hosts the Centre for Literature, Culture & Place which explores literature and its relationships with geography, space, landscape, travel, architecture, and the environment.

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High dimensional search using polyhedral query

Connor, Richard and MacKenzie-Leigh, Stewart and Moss, Robert (2014) High dimensional search using polyhedral query. In: Similarity Search and Applications. Lecture Notes in Computer Science, 8821 . Springer-Verlag, pp. 176-188. ISBN 9783319119878

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Abstract

It is well known that, as the dimensionality of a metric space increases, metric search techniques become less effective and the cost of indexing mechanisms becomes greater than the saving they give. This is due to the so-called curse of dimensionality. One effect of increasing dimensionality is that the ratio of unit hypersphere to unit hypercube volume decreases rapidly, making the solution to a similarity query (the query ball, or hypersphere) ever more difficult to identify by using metric invariants such as triangle inequality. In this paper we take a different approach, by identifying points within a query polyhedron rather than a ball. We show how this can be achieved by constructing a surrogate metric space, such that a query ball in the surrogate space corresponds to a polyhedron in the original space. If the polyhedron contains the ball, the overall cost of the query is likely to be increased in high dimensions; however, we show that shrinking the polyhedron can capture a surprisingly high proportion of the points within the ball, whilst at the same time giving a more efficient, and more scalable, search. We show results which confirm our underlying hypothesis. In some cases we can retrieve significant volumes of query results from spaces which are otherwise intractable.