Learning Kernels from genetic profiles to discriminate tumor subtypes

Our work aims to perform the feature selection step on Multiple Kernel Learning by optimizing the Kernel Target Alignment score. It begins by building feature-wise gaussian kernel functions. Then by a constrained linear combination of the feature-wise kernels, we aim to increase the Kernel Target Alignment to obtain a new optimized custom kernel. The linear combination results in a sparse solution where only few kernels survive to improve KTA and consequently a reduced feature subset is obtained. Reducing considerably the original gene set allow to study deeper the selected genes for clinical purposes. The higher the KTA obtained, the better the feature selection, since we want to build custom kernels to use them for classification purposes later. The final kernel after optimizing the KTA is built by a linear combination of ‘Ki’ kernels, each one associated to a μi coefficient. The μ vector is computed during the optimization process.Sociedad Argentina de Informática e Investigación Operativ

Learning Kernels from genetic profiles to discriminate tumor subtypes

Our work aims to perform the feature selection step on Multiple Kernel Learning by optimizing the Kernel Target Alignment score. It begins by building feature-wise gaussian kernel functions. Then by a constrained linear combination of the feature-wise kernels, we aim to increase the Kernel Target Alignment to obtain a new optimized custom kernel. The linear combination results in a sparse solution where only few kernels survive to improve KTA and consequently a reduced feature subset is obtained. Reducing considerably the original gene set allow to study deeper the selected genes for clinical purposes. The higher the KTA obtained, the better the feature selection, since we want to build custom kernels to use them for classification purposes later. The final kernel after optimizing the KTA is built by a linear combination of ‘Ki’ kernels, each one associated to a μi coefficient. The μ vector is computed during the optimization process.Sociedad Argentina de Informática e Investigación Operativ

Learning Kernels from genetic profiles to discriminate tumor subtypes

Our work aims to perform the feature selection step on Multiple Kernel Learning by optimizing the Kernel Target Alignment score. It begins by building feature-wise gaussian kernel functions. Then by a constrained linear combination of the feature-wise kernels, we aim to increase the Kernel Target Alignment to obtain a new optimized custom kernel. The linear combination results in a sparse solution where only few kernels survive to improve KTA and consequently a reduced feature subset is obtained. Reducing considerably the original gene set allow to study deeper the selected genes for clinical purposes. The higher the KTA obtained, the better the feature selection, since we want to build custom kernels to use them for classification purposes later. The final kernel after optimizing the KTA is built by a linear combination of ‘Ki’ kernels, each one associated to a μi coefficient. The μ vector is computed during the optimization process.Sociedad Argentina de Informática e Investigación Operativ

Boosting diagnosis accuracy of Alzheimer's disease using statistical and kernel-based feature selection techniques

Alzheimer's disease (AD) is the most common type of dementia in the elderly. Approximately, 26 million people worldwide are affected by AD. Among the various diagnostic methods for Alzheimer's disease, MRI brain imaging can display sharp changes in brain tissues. It can be used as a method for early diagnosis of Alzheimer's disease. Considering the high volume of features related to brain tissue thickness, requires the using feature reduction methods. For this purpose, statistical tests pair sample test and Independent sample test was used. After careful selection of key features, for reducing the number of features, SAS which is a kernel-based feature selection algorithm is used in linear and nonlinear mode. At the end, neural network classification, decision trees, nearest neighbor and Naïve Bayes algorithms are used for modeling. Results show that the classification accuracy of obtained feature subsets have better results compare to the original data set

Learning Discriminative Stein Kernel for SPD Matrices and Its Applications

Stein kernel has recently shown promising performance on classifying images represented by symmetric positive definite (SPD) matrices. It evaluates the similarity between two SPD matrices through their eigenvalues. In this paper, we argue that directly using the original eigenvalues may be problematic because: i) Eigenvalue estimation becomes biased when the number of samples is inadequate, which may lead to unreliable kernel evaluation; ii) More importantly, eigenvalues only reflect the property of an individual SPD matrix. They are not necessarily optimal for computing Stein kernel when the goal is to discriminate different sets of SPD matrices. To address the two issues in one shot, we propose a discriminative Stein kernel, in which an extra parameter vector is defined to adjust the eigenvalues of the input SPD matrices. The optimal parameter values are sought by optimizing a proxy of classification performance. To show the generality of the proposed method, three different kernel learning criteria that are commonly used in the literature are employed respectively as a proxy. A comprehensive experimental study is conducted on a variety of image classification tasks to compare our proposed discriminative Stein kernel with the original Stein kernel and other commonly used methods for evaluating the similarity between SPD matrices. The experimental results demonstrate that, the discriminative Stein kernel can attain greater discrimination and better align with classification tasks by altering the eigenvalues. This makes it produce higher classification performance than the original Stein kernel and other commonly used methods.Comment: 13 page

Riemannian approaches in Brain-Computer Interfaces: a review

International audienceAlthough promising from numerous applications, current Brain-Computer Interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of ElectroEncephaloGraphic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning

A study on multi-scale kernel optimisation via centered kernel-target alignment

Kernel mapping is one of the most widespread approaches to intrinsically deriving nonlinear classifiers. With the aim of better suiting a given dataset, different kernels have been proposed and different bounds and methodologies have been studied to optimise them. We focus on the optimisation of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, although it has been shown to achieve better performance in the presence of heterogeneous attributes. The large number of parameters in multi-scale kernels makes it computationally unaffordable to optimise them by applying traditional cross-validation. Instead, an analytical measure known as centered kernel-target alignment (CKTA) can be used to align the kernel to the so-called ideal kernel matrix. This paper analyses and compares this and other alternatives, providing a review of the literature in kernel optimisation and some insights into the usefulness of multi-scale kernel optimisation via CKTA. When applied to the binary support vector machine paradigm (SVM), the results using 24 datasets show that CKTA with a multi-scale kernel leads to the construction of a well-defined feature space and simpler SVM models, provides an implicit filtering of non-informative features and achieves robust and comparable performance to other methods even when using random initialisations. Finally, we derive some considerations about when a multi-scale approach could be, in general, useful and propose a distance-based initialisation technique for the gradient-ascent method, which shows promising results

Multiclass Feature Selection with Kernel Gram-matrix-based criteria

Feature selection has been an important issue during the last decades to determine the most relevant features according to a given classification problem. Numerous methods emerged that take into account Support Vector Machines in the selection process. Such approaches are powerful but often complex and costly. In this paper, we propose new feature selection methods based on two criteria designed for the optimization of SVM: Kernel Target Alignment and Kernel Class Separability. We demonstrate how these two measures, when fully expressed, can build efficient and simple methods, easily applicable to multiclass problems, and iteratively computable with minimal memory requirements. An extensive experimental study is conducted both on artificial and real-world data sets to compare the proposed methods to state of the art feature selection algorithms. The results demonstrate the relevance of the proposed methods both in terms of performance and computational cost