2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-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
SessionMLSP-37: Pattern Recognition and Classification 2
LocationGather.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
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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.