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MILP-Based Unsupervised Clustering
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DOI：10.1109/LSP.2018.2877056.png)
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摘要
En 中文
In this letter, we discuss the problem of unsupervised clustering of sensor signals based on their information content. In the past, the problem has been formulated as a matrix factorization problem and has been solved with different variants of gradient descent. We reformulate the nonconvex cost function as a mixed integer linear programing problem with explicit clustering constraints and solve it with branch and bound, while introducing a scalable variant to reduce the computational time. The proposed method is applied to clustering problems in hyperspectral imaging and multiview image clustering and extensive results have been presented demonstrating the superiority of the novel framework over existing alternatives.
Keyword:
Mixed integer linear programming
matrix factorization
clustering
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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引用论文
Two algorithms for orthogonal nonnegative matrix factorization with application to clustering两种正交非负矩阵分解算法及其在聚类中的应用.png)
NEUROCOMPUTINGOAAI
NEUROCOMPUTINGIF6.5
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Data-driven sensors clustering and filtering for communication efficient field reconstructionSIGNAL PROCESSINGOAAI
SIGNAL PROCESSINGIF3.6
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