Erratum to: A Method for Metric Learning with Multiple-Kernel Embedding
نویسندگان
چکیده
منابع مشابه
Semi-supervised clustering with metric learning: An adaptive kernel method
Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel methods for semi-supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided, but also lack a measure that achieves better effectiven...
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Kernel regression is a well-established method for nonlinear regression in which the target value for a test point is estimated using a weighted average of the surrounding training samples. The weights are typically obtained by applying a distance-based kernel function to each of the samples, which presumes the existence of a well-defined distance metric. In this paper, we construct a novel alg...
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Fréchet’s classical isometric embedding argument has evolved to become a major tool in the study of metric spaces. An important example of a Fréchet embedding is Bourgain’s embedding [4]. The authors have recently shown [2] that for every ε > 0 any n-point metric space contains a subset of size at least n1−ε which embeds into `2 with distortion O ( log(2/ε) ε ) . The embedding used in [2] is no...
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We use an analytic center cutting plane method (ACCPM) to solve the multiple kernel learning problem. ACCPM has linear convergence but requires very few gradient evaluations, which makes it particularly efficient on large sample sizes. We compare the numerical performance of this algorithm with another recent first-order algorithm on several data sets and use multiple kernel learning to predict...
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Most metric learning algorithms, as well as Fisher’s Discriminant Analysis (FDA), optimize some cost function of different measures of within-and between-class distances. On the other hand, Support Vector Machines(SVMs) and several Multiple Kernel Learning (MKL) algorithms are based on the SVM large margin theory. Recently, SVMs have been analyzed from a metric learning perspective, and formula...
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ژورنال
عنوان ژورنال: Neural Processing Letters
سال: 2015
ISSN: 1370-4621,1573-773X
DOI: 10.1007/s11063-015-9468-8