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A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction. (arXiv:2202.02904v2 [cs.LG] UPDATED)
Nov. 1, 2022, 1:12 a.m. | Ming-Jun Lai, Zhaiming Shen
cs.LG updates on arXiv.org arxiv.org
A least squares semi-supervised local clustering algorithm based on the idea
of compressed sensing is proposed to extract clusters from a graph with known
adjacency matrix. The algorithm is based on a two-stage approach similar to the
one in \cite{LaiMckenzie2020}. However, under a weaker assumption and with less
computational complexity than the one in \cite{LaiMckenzie2020}, the algorithm
is shown to be able to find a desired cluster with high probability. The ``one
cluster at a time" feature of our method …
arxiv cluster extraction least semi-supervised sensing squares
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