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• W1510402911 abstract "Here, we are interested in obtaining a two-dimensional embedding of face-pose images that preserves their local structure captured by the pair-wise distances among them by using multidimensional scaling (MDS). The MDS problem is formulated as maximization of a correntropy criterion, which is solved by half-quadratic optimization in a multiplicative formulation. By doing so, theMDS copes with an initial dissimilarity matrix contaminated with outliers, because the correntropy criterion is closely related to the Welsch M-estimator. The proposed algorithm is coined as Multiplicative Half-Quadratic MDS (MHQMDS). Its performance is assessed for potential functions associated to various M-estimators have been tested. Three state-of-the-art MDS techniques, namely the Scaling by Majorizing a Complicated Function (SMACOF), the Robust Euclidean Embedding (REE), and the Robust MDS (RMDS), are implemented under the same conditions. The experimental results indicate that the MHQMDS, outperforms the aforementioned state-of-the-art competing techniques." @default.
• W1510402911 created "2016-06-24" @default.
• W1510402911 creator A5051404366 @default.
• W1510402911 creator A5065184406 @default.
• W1510402911 date "2015-03-01" @default.
• W1510402911 modified "2023-09-26" @default.
• W1510402911 title "Correntropy based robust multidimensional scaling applied to faces" @default.
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• W1510402911 doi "https://doi.org/10.1109/iwbf.2015.7110227" @default.
• W1510402911 hasPublicationYear "2015" @default.
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