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Kernel Spectral Clustering (KSC) solves a weighted kernel principal component analysis problem in a primal-dual optimization framework. It builds an unsupervised model on a small subset of data using the dual solution of the optimization problem. This allows KSC to have a powerful out-of-sample extension property leading to good cluster generalization w.r.t. unseen data points. However, in the presence...
We propose FS-Scala, a flexible and modular Scala based implementation of the Fixed Size Least Squares Support Vector Machine (FS-LSSVM) for large data sets. The framework consists of a set of modules for (gradient and gradient free) optimization, model representation, kernel functions and evaluation of FS-LSSVM models. A kernel based Fixed-Size Least Squares Support Vector Machine (FS-LSSVM) model...
This paper proposes a specialized decomposition algorithm, which incorporates the interior-point method into an augmented Lagrangian decomposition technique, for optimizing the quality of service through a data network. We show that many routing or resource allocation problems from the network literature can be recast into separable problems for which our algorithm can be applied. The proposed algorithm...
In this paper we study the problem of finding a support of unknown high-dimensional distributions in the presence of labeling information, called Supervised Novelty Detection (SND). The One-Class Support Vector Machine (SVM) is a widely used kernel-based technique to address this problem. However with the latter approach it is difficult to model a mixture of distributions from which the support might...
In this paper we present a novel approach and a new machine learning problem, called Supervised Novelty Detection (SND). This problem extends the One-Class Support Vector Machine setting for binary classification while keeping the nice properties of novelty detection problem at hand. To tackle this we approach binary classification from a new perspective using two different estimators and a coupled...
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