Random projections are able to perform dimension reduction efficiently for datasets with nonlinear low-dimensional structures. One well-known example is that random matrices embed sparse vectors into a subspace nearly isometrically, known as the restricted isometric property in compressed sensing. In this paper, we explore some applications of deep neural networks. We provide expressive power f...

Given a random vector X, we address the question of linear separability of X, that is, the task of finding a linear operator W such that we have (S1, . . . ,SM ) = (WX) with statistically independent random vectors Si. As this requirement alone is already fulfilled trivially by X being independent of the empty rest, we require that the components be not further decomposable. We show that if X h...

A new optimization technique is proposed for classifiers fusion — Cooperative Coevolutionary Ensemble Learning (CCEL). It is based on a specific multipopulational evolutionary algorithm — cooperative coevolution. It can be used as a wrapper over any kind of weak algorithms, learning procedures and fusion functions, for both classification and regression tasks. Experiments on the real-world prob...

A well-known method for completing low-rank matrices based on convex optimization has been established by Candès and Recht [1]. Although theoretically complete, the method may not entirely solve the low-rank matrix completion problem. This is because the method captures only the low-rankness property which gives merely a constraint that the data points locate on some lowdimensional subspace, bu...

In our current research project about electronic designs the problem to retrieve attribute dependent generalized cases occurs. This kind of cases cover an arbitrary subspace rather than a point in the space, spanned by the cases attributes and by a set of constraints. For such representations, the similarity assessment between a point query and generalized cases is a difficult problem for which...

We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role of search direction. For discriminant ...

In this work, we present a novel trained method for combining biometric matchers at the score level. The new method is based on a combination of machine learning classifiers trained using the match scores from different biometric approaches as features. The parameters of a finite Gaussian mixture model are used for modelling the genuine and impostor score densities during the fusion step. Sever...

We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include...

In this study, a random subspace-based function tree (RSFT) was developed for landslide susceptibility modeling, and by comparing with bagging-based (BFT), classification regression (CART), Naïve-Bayes (NBTree) Classifier, to judge the performance difference between hybrid model single models. first step, according characteristics of geological environment previous literature, 12 conditioning f...