Ising-Based Combinatorial Clustering Using the Kernel Method

Kumagai, Masahito; Komatsu, Kazuhiko; Sato, Masayuki; Kobayashi, Hiroaki

doi:10.1109/mcsoc51149.2021.00037

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Investigating the Characteristics of Ising Machines

Komatsu,

Onoda,

Kumagai

et al. 2023

2023 IEEE International Conference on Quantum Computing and Engineering (QCE)

View full text Add to dashboard Cite

Investigating the Characteristics of Ising Machines

Komatsu,

Onoda,

Kumagai

et al. 2023

2023 IEEE International Conference on Quantum Computing and Engineering (QCE)

View full text Add to dashboard Cite

Ising-Based Kernel Clustering

et al. 2023

View full text Add to dashboard Cite

Combinatorial clustering based on the Ising model is drawing attention as a high-quality clustering method. However, conventional Ising-based clustering methods using the Euclidean distance cannot handle irregular data. To overcome this problem, this paper proposes an Ising-based kernel clustering method. The kernel clustering method is designed based on two critical ideas. One is to perform clustering of irregular data by mapping the data onto a high-dimensional feature space by using a kernel trick. The other is the utilization of matrix–matrix calculations in the numerical libraries to accelerate preprocess for annealing. While the conventional Ising-based clustering is not designed to accept the transformed data by the kernel trick, this paper extends the availability of Ising-based clustering to process a distance matrix defined in high-dimensional data space. The proposed method can handle the Gram matrix determined by the kernel method as a high-dimensional distance matrix to handle irregular data. By comparing the proposed Ising-based kernel clustering method with the conventional Euclidean distance-based combinatorial clustering, it is clarified that the quality of the clustering results of the proposed method for irregular data is significantly better than that of the conventional method. Furthermore, the preprocess for annealing by the proposed method using numerical libraries is by a factor of up to 12.4 million × from the conventional naive python’s implementation. Comparisons between Ising-based kernel clustering and kernel K-means reveal that the proposed method has the potential to obtain higher-quality clustering results than the kernel K-means as a representative of the state-of-the-art kernel clustering methods.

show abstract

Ising-Based Combinatorial Clustering Using the Kernel Method

Cited by 2 publications

References 14 publications

Investigating the Characteristics of Ising Machines

Investigating the Characteristics of Ising Machines

Ising-Based Kernel Clustering

Contact Info

Product

Resources

About