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- W2495621880 abstract "Entropy weighting used in some soft subspace clustering algorithms is sensitive to the scaling parameter. In this paper, we propose a novel soft subspace clustering algorithm by using log-transformed distances in the objective function. The proposed algorithm allows users to choose a value of the scaling parameter easily because the entropy weighting in the proposed algorithm is less sensitive to the scaling parameter. In addition, the proposed algorithm is less sensitive to noises because a point far away from its cluster center is given a small weight in the cluster center calculation. Experiments on both synthetic datasets and real datasets are used to demonstrate the performance of the proposed algorithm." @default.
- W2495621880 created "2016-08-23" @default.
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- W2495621880 date "2015-09-01" @default.
- W2495621880 modified "2023-10-14" @default.
- W2495621880 title "A soft subspace clustering algorithm with log-transformed distances" @default.
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- W2495621880 doi "https://doi.org/10.3934/bdia.2016.1.93" @default.
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