Multiple kernel learning

About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.

Multiple subject learning for inter-subject prediction

Abstract: Multi-voxel pattern analysis has become an important tool for neuroimaging data analysis by allowing to predict a behavioral variable from the imaging patterns. However, standard models do not take into account the differences that can exist between subjects, so that they perform poorly in the inter-subject prediction task. We here introduce a model called Multiple Subject Learning (MSL) that is designed to effectively combine the information provided by fMRI data from several subjects; in a first stage, a weighting of single-subject kernels is learnt using multiple kernel learning to produce a classifier; then, a data shuffling procedure allows to build ensembles of such classifiers, which are then combined by a majority vote. We show that MSL outperforms other models in the inter-subject prediction task and we discuss the empirical behavior of this new model.

FTWSVM-SR: DNA-Binding Proteins Identification via Fuzzy Twin Support Vector Machines on Self-Representation.

Abstract: Due to the high cost of DNA-binding proteins (DBPs) detection, many machine learning algorithms (ML) have been utilized to large-scale process and detect DBPs. The previous methods took no count of the processing of noise samples. In this study, a fuzzy twin support vector machine (FTWSVM) is employed to detect DBPs. First, multiple types of protein sequence features are formed into kernel matrices; Then, multiple kernel learning (MKL) algorithm is utilized to linear combine multiple kernels; next, self-representation-based membership function is utilized to estimate membership value (weight) of each training sample; finally, we feed the integrated kernel matrix and membership values into the FTWSVM-SR model for training and testing. On comparison with other predictive models, FTWSVM based on SR (FTWSVM-SR) obtains the best performance of Matthew's correlation coefficient (MCC): 0.7410 and 0.5909 on two independent testing sets (PDB186 and PDB2272 datasets), respectively. The results confirm that our method can be an effective DBPs detection tool. Before the biochemical experiment, our model can screen and analyze DBPs on a large scale.

Feature Group Selection Using MKL Penalized with \(\ell _1\)-norm and SVM as Base Learner

Abstract: Objective feature selection is an important component in the machine learning framework, which has addressed problems like computational burden increasing and unnecessary high-dimensional representations. Most of feature selection techniques only perform individual feature evaluations and ignore the structural relationships between features of the same nature, causing relations to break and harming the algorithm performance. In this paper a feature group selection technique is proposed with the aim of objectively identify the relevance that a feature group carries out in a classification task. The proposed method uses Multiple Kernel Learning with a penalization rule based on the \(\ell _1\)-norm and a Support Vector Machine as base learner. Performance evaluation is carried out using two binarized configurations of the freely available MFEAT dataset. It provides six different feature groups allowing to develop multiple feature group analysis. The experimental results show that the implemented methodology is stable in the identification of the relevance of each feature group during all experiments, what allows to outperform the classification accuracy of state-of-the-art methods.

Hierarchical feature concatenation-based kernel sparse representations for image categorization

Abstract: In order to obtain improved performance in complicated visual categorization tasks, considerable research has adopted multiple kernel learning based on dozens of different features. However, it is a complex process that needs to extract a multitude of features and seeks the optimal combination of multiple kernels. Inspired by the key idea of hierarchical learning, in this paper, we propose to find sparse representation based on feature concatenation using hierarchical kernel orthogonal matching pursuit (HKOMP). In addition to commonly used spatial pyramid feature for kernel representation, our method only employs one type of generic image feature, i.e., p.d.f gradient-based orientation histogram for concatenation of sparse codes. Next, the resulting concatenated features kernelized with widely used Gaussian radial basis kernel function form compact sparse representations in the second layer for linear support vector machine. HKOMP algorithm combines the advantages of building image representations layer-by-layer and kernel learning. Several publicly available image datasets are used to evaluate the presented approach and empirical results for various datasets show that the proposed scheme outperforms many kernel learning based and other competitive image categorization algorithms.

Use of MKL as symbol classifier for Gujarati character recognition

Abstract: The present work is part of ongoing effort to improve the performance of Gujarati character recognition. In the recent advancement in kernel methods, the novel concept of multiple kernel learning(MKL) has given improved results for many problems. In this paper, we present novel application of MKL for Gujarati character recognition. We have applied three different feature representations for symbols obtained after zone wise segmentation of Gujarati text. The MKL based classification is proposed, where the MKL is used for learning optimal combination of different features for classification. In addition MKL based classification results for different features is also presented. The multiclass classification is performed in Decision DAG framework. The comparison results in 1-Vs-1 framework and using KNN classifier is also presented. The experiments have shown substantial improvement in earlier results.