| Paper ID | MLSP-37.1 |
| Paper Title |
Unified Clustering and Outlier Detection on Specialized Hardware |
| Authors |
Eldan Cohen, University of Toronto, Canada; Hayato Ushijima-Mwesigwa, Avradip Mandal, Arnab Roy, Fujitsu Laboratories of America, United States |
| Session | MLSP-37: Pattern Recognition and Classification 2 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-PRCL] Pattern recognition and classification |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Clustering and outlier detection are often studied as separate problems. However, previous work has shown that a unified approach can lead to better performance. Unified clustering and outlier detection is a hard combinatorial problem that has received significant attention in recent years. The recent emergence of specialized optimization hardware capable of solving combinatorial problems formulated as Quadratic Unconstrained Binary Optimization (QUBO) models has led to increased interest in harnessing these platforms in core data mining tasks. In this work, we present a novel QUBO formulation of the unified clustering and outlier detection problem and use the Fujitsu Digital Annealer, a specialized CMOS hardware, to solve it. Experiments on synthetic and real datasets demonstrate the effectiveness of our approach. |
