Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

Clustering of data with missing entries

Abstract: 

The analysis of large datasets is often complicated by the presence of missing entries, mainly because most of the current machine learning algorithms are designed to work with full data. The main focus of this work is to introduce a clustering
algorithm, that will provide good clustering even in the presence of missing data. The proposed technique solves an l0 fusion penalty based optimization problem to recover the clusters. We theoretically analyze the conditions needed for the successful recovery of the clusters. We also propose an algorithm to solve a relaxation of this problem using saturating non-convex fusion penalties. The method is demonstrated on simulated and real datasets, and is observed to perform well in the presence of large fractions of missing entries.

up
0 users have voted:

Paper Details

Authors:
Sunrita Poddar, Mathews Jacob
Submitted On:
14 April 2018 - 8:11pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Sunrita Poddar
Paper Code:
MLSP-P10.5
Document Year:
2018
Cite

Document Files

clusteringMissingEntries

(74 downloads)

icassp18poster.pdf

(116 downloads)

Subscribe

[1] Sunrita Poddar, Mathews Jacob, "Clustering of data with missing entries", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2858. Accessed: Dec. 11, 2018.
@article{2858-18,
url = {http://sigport.org/2858},
author = {Sunrita Poddar; Mathews Jacob },
publisher = {IEEE SigPort},
title = {Clustering of data with missing entries},
year = {2018} }
TY - EJOUR
T1 - Clustering of data with missing entries
AU - Sunrita Poddar; Mathews Jacob
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2858
ER -
Sunrita Poddar, Mathews Jacob. (2018). Clustering of data with missing entries. IEEE SigPort. http://sigport.org/2858
Sunrita Poddar, Mathews Jacob, 2018. Clustering of data with missing entries. Available at: http://sigport.org/2858.
Sunrita Poddar, Mathews Jacob. (2018). "Clustering of data with missing entries." Web.
1. Sunrita Poddar, Mathews Jacob. Clustering of data with missing entries [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2858