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In the current work we aim to combine the flexibility of dissimilarity representations with the discriminative ability of the well-established sparse ...
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We demonstrate that CR-based methods have a clear advantage on dissimilarity data stemming from extended objects, manifold structures or a combination of these ...
Clustering of non-vectorial data constitutes one active topic of research. Here, data are characterized by pairwise data dissimilarities instead of a direct.
Put simply, sparse representations represent most or all information contained in a data with a weighted linear combination of a small number of elements or ...
Missing: Dissimilarity | Show results with:Dissimilarity
Abstract. Traditional sparse representation algorithms usually operate in a single Euclidean space. This paper leverages a self-explanatory re-.
Missing: Dissimilarity | Show results with:Dissimilarity
Jul 25, 2014 · Once all models are learned, we compute the dissimilarity between each model and a data point by the absolute value of the representation error.
Mar 1, 2021 · The image classification system presented here trains classifiers to predict patterns within a vector space by combining the dissimilarity ...
Apr 9, 2016 · Dissimilarities in this case, can be representation or coding errors of data via models. On the other hand, X and Y can consist of the same type ...
In this work we perform a thorough evaluation of the most popular CR-based classification scheme, the SRC, on the task of classification in dissimilarity ...
For instance, with the continuous of the big data challenge, how to integrate the sparse representation with complex data analysis tasks such as imbalanced data.