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MILP-Based Unsupervised Clustering
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DOI：10.1109/LSP.2018.2877056.png)
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Abstract
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.
Keywords:
Mixed integer linear programming
matrix factorization
clustering
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