Semi-Supervised Affinity Propagation with Instance-Level Constraints

Inmar Givoni, Brendan Frey
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:161-168, 2009.

Abstract

Recently, affinity propagation (AP) was introduced as an unsupervised learning algorithm for exemplar based clustering. Here we extend the AP model to account for semi-supervised clustering. AP, which is formulated as inference in a factor-graph, can be naturally extended to account for ?instance-level? constraints: pairs of data points that cannot belong to the same cluster (cannot-link), or must belong to the same cluster (must-link). We present a semi-supervised AP algorithm (SSAP) that can use instance-level constraints to guide the clustering. We demonstrate the applicability of SSAP to interactive image segmentation by using SSAP to cluster superpixels while taking into account user instructions regarding which superpixels belong to the same object. We demonstrate SSAP can achieve better performance compared to other semi-supervised methods.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-givoni09a, title = {Semi-Supervised Affinity Propagation with Instance-Level Constraints}, author = {Inmar Givoni and Brendan Frey}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {161--168}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/givoni09a/givoni09a.pdf}, url = {http://proceedings.mlr.press/v5/givoni09a.html}, abstract = {Recently, affinity propagation (AP) was introduced as an unsupervised learning algorithm for exemplar based clustering. Here we extend the AP model to account for semi-supervised clustering. AP, which is formulated as inference in a factor-graph, can be naturally extended to account for ?instance-level? constraints: pairs of data points that cannot belong to the same cluster (cannot-link), or must belong to the same cluster (must-link). We present a semi-supervised AP algorithm (SSAP) that can use instance-level constraints to guide the clustering. We demonstrate the applicability of SSAP to interactive image segmentation by using SSAP to cluster superpixels while taking into account user instructions regarding which superpixels belong to the same object. We demonstrate SSAP can achieve better performance compared to other semi-supervised methods.} }
Endnote
%0 Conference Paper %T Semi-Supervised Affinity Propagation with Instance-Level Constraints %A Inmar Givoni %A Brendan Frey %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-givoni09a %I PMLR %J Proceedings of Machine Learning Research %P 161--168 %U http://proceedings.mlr.press %V 5 %W PMLR %X Recently, affinity propagation (AP) was introduced as an unsupervised learning algorithm for exemplar based clustering. Here we extend the AP model to account for semi-supervised clustering. AP, which is formulated as inference in a factor-graph, can be naturally extended to account for ?instance-level? constraints: pairs of data points that cannot belong to the same cluster (cannot-link), or must belong to the same cluster (must-link). We present a semi-supervised AP algorithm (SSAP) that can use instance-level constraints to guide the clustering. We demonstrate the applicability of SSAP to interactive image segmentation by using SSAP to cluster superpixels while taking into account user instructions regarding which superpixels belong to the same object. We demonstrate SSAP can achieve better performance compared to other semi-supervised methods.
RIS
TY - CPAPER TI - Semi-Supervised Affinity Propagation with Instance-Level Constraints AU - Inmar Givoni AU - Brendan Frey BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-givoni09a PB - PMLR SP - 161 DP - PMLR EP - 168 L1 - http://proceedings.mlr.press/v5/givoni09a/givoni09a.pdf UR - http://proceedings.mlr.press/v5/givoni09a.html AB - Recently, affinity propagation (AP) was introduced as an unsupervised learning algorithm for exemplar based clustering. Here we extend the AP model to account for semi-supervised clustering. AP, which is formulated as inference in a factor-graph, can be naturally extended to account for ?instance-level? constraints: pairs of data points that cannot belong to the same cluster (cannot-link), or must belong to the same cluster (must-link). We present a semi-supervised AP algorithm (SSAP) that can use instance-level constraints to guide the clustering. We demonstrate the applicability of SSAP to interactive image segmentation by using SSAP to cluster superpixels while taking into account user instructions regarding which superpixels belong to the same object. We demonstrate SSAP can achieve better performance compared to other semi-supervised methods. ER -
APA
Givoni, I. & Frey, B.. (2009). Semi-Supervised Affinity Propagation with Instance-Level Constraints. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:161-168

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