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Published in:
2019 | OriginalPaper | Chapter
Principal Sample Analysis for Data Ranking
Author : Benyamin Ghojogh
Published in: Advances in Artificial Intelligence
Publisher: Springer International Publishing
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Abstract
Because of the ever growing amounts of data, challenges have appeared for storage and processing, making data reduction still an important field of study. Numerosity reduction or prototype selection is one of the primary methods of data reduction. In this paper, we propose some possible improvements for Principal Sample Analysis (PSA) which is a numerosity reduction algorithm. The improvements are PSA in Hilbert space, improving its time complexity using anchor points, sample size estimation using PAC learning, and PSA for regression and clustering tasks.