Similarity search in high-dimensional spaces is popular for applications like image processing, time series, and genome data. In higher dimensions, the phenomenon of curse of dimensionality kills the effectiveness of most of the index structures, giving way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity searches. In addition to range searches and k-nearest neighbor searches, there is a need to answer negative queries formed by excluded regions, in high-dimensional data. Though there have been a slew of variants of LSH to improve efficiency, reduce storage, and provide better accuracies, none of the techniques are capable of answering ...
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
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