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.
B. Ghojogh—Supervised by Dr. Mark Crowley and Dr. Fakhri Karray, Department of Electrical and Computer Engineering, {mcrowley, karray}@uwaterloo.ca.
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Ghojogh, B. (2019). Principal Sample Analysis for Data Ranking. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_62
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DOI: https://doi.org/10.1007/978-3-030-18305-9_62
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