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Improved Subspace K-Means Performance via a Randomized Matrix Decomposition

Abstract: 

Subspace clustering algorithms provide the capability
to project a dataset onto bases that facilitate clustering.
Proposed in 2017, the subspace k-means algorithm simultaneously
performs clustering and dimensionality reduction with the goal
of finding the optimal subspace for the cluster structure; this
is accomplished by incorporating a trade-off between cluster
and noise subspaces in the objective function. In this study,
we improve subspace k-means by estimating a critical transformation
matrix via a randomized eigenvalue decomposition.
Our modification results in an order of magnitude runtime
improvement on high dimensional data, while retaining the
simplicity, interpretable subspace projections, and convergence
guarantees of the original algorithm.

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Paper Details

Authors:
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley
Submitted On:
14 November 2019 - 7:39pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Trevor Vannoy
Document Year:
2019
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Improved Subspace K-means Performance via a Randomized Matrix Decomposition.pdf

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[1] Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley, "Improved Subspace K-Means Performance via a Randomized Matrix Decomposition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4958. Accessed: Dec. 12, 2019.
@article{4958-19,
url = {http://sigport.org/4958},
author = {Trevor Vannoy; Jacob Senecal; Veronika Strnadova-Neeley },
publisher = {IEEE SigPort},
title = {Improved Subspace K-Means Performance via a Randomized Matrix Decomposition},
year = {2019} }
TY - EJOUR
T1 - Improved Subspace K-Means Performance via a Randomized Matrix Decomposition
AU - Trevor Vannoy; Jacob Senecal; Veronika Strnadova-Neeley
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4958
ER -
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. (2019). Improved Subspace K-Means Performance via a Randomized Matrix Decomposition. IEEE SigPort. http://sigport.org/4958
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley, 2019. Improved Subspace K-Means Performance via a Randomized Matrix Decomposition. Available at: http://sigport.org/4958.
Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. (2019). "Improved Subspace K-Means Performance via a Randomized Matrix Decomposition." Web.
1. Trevor Vannoy, Jacob Senecal, Veronika Strnadova-Neeley. Improved Subspace K-Means Performance via a Randomized Matrix Decomposition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4958