Instance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace Learning

Canadian Conference on Artificial Intelligence 2019 Benyamin GhojoghMark Crowley

One way to deal with the ever increasing amount of available data for processing is to rank data instances by usefulness and reduce the dataset size. In this work, we introduce a framework to achieve this using matrix decomposition and subspace learning... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.