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• W2547406430 abstract "Multidimensional scaling (MDS) refers to a class of dimensionality reduction techniques, which represent entities as points in a low-dimensional space so that the interpoint distances approximate the initial pairwise dissimilarities between entities as closely as possible. The traditional methods for solving MDS are susceptible to outliers. Here, a unified framework is proposed, where the MDS is treated as maximization of a correntropy criterion, which is solved by half-quadratic optimization in either multiplicative or additive forms. By doing so, MDS can cope with an initial dissimilarity matrix contaminated with outliers because the correntropy criterion is closely related to M-estimators. Three novel algorithms are derived. Their performances are assessed experimentally against three state-of-the-art MDS techniques, namely the scaling by majorizing a complicated function, the robust Euclidean embedding, and the robust MDS under the same conditions. The experimental results indicate that the proposed algorithms perform substantially better than the aforementioned competing techniques." @default.
• W2547406430 created "2016-11-11" @default.
• W2547406430 creator A5051404366 @default.
• W2547406430 creator A5065184406 @default.
• W2547406430 date "2017-02-15" @default.
• W2547406430 modified "2023-09-26" @default.
• W2547406430 title "Robust Multidimensional Scaling Using a Maximum Correntropy Criterion" @default.
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• W2547406430 doi "https://doi.org/10.1109/tsp.2016.2625265" @default.
• W2547406430 hasPublicationYear "2017" @default.
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