IEEE DSW · 2018
Robust and Consistent Clustering Recovery via SDP Approaches
IEEE Data Science Workshop
Can clusters be recovered from noisy similarities without knowing their number?
A semidefinite programming approach recovers clustering structure in the presence of noise and outliers. The analysis gives conditions for exact recovery and consistency in a Gaussian similarity model, supported by synthetic experiments.
Research themes
Cite this paper
Chenxi Sun, Tongxin Li, Victor O. K. Li. Robust and Consistent Clustering Recovery via SDP Approaches. IEEE Data Science Workshop, 2018. https://doi.org/10.1109/DSW.2018.8439918
BibTeX
@inproceedings{tongxin-clustering-sdp,
title = {{Robust and Consistent Clustering Recovery via SDP Approaches}},
author = {Chenxi Sun and Tongxin Li and Victor O. K. Li},
year = {2018},
booktitle = {IEEE Data Science Workshop},
url = {https://doi.org/10.1109/DSW.2018.8439918},
doi = {10.1109/DSW.2018.8439918}
}
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