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.

MKL-RT: Multiple Kernel Learning for Ratio-trace Problems via Convex Optimization.

Abstract: In the recent past, automatic selection or combination of kernels (or features) based on multiple kernel learning (MKL) approaches has been receiving significant attention from various research communities. Though MKL has been extensively studied in the context of support vector machines (SVM), it is relatively less explored for ratio-trace problems. In this paper, we show that MKL can be formulated as a convex optimization problem for a general class of ratio-trace problems that encompasses many popular algorithms used in various computer vision applications. We also provide an optimization procedure that is guaranteed to converge to the global optimum of the proposed optimization problem. We experimentally demonstrate that the proposed MKL approach, which we refer to as MKL-RT, can be successfully used to select features for discriminative dimensionality reduction and cross-modal retrieval. We also show that the proposed convex MKL-RT approach performs better than the recently proposed non-convex MKL-DR approach.

A Novel Multiple Kernel Learning Framework for Remote Sensing Scene Classification

Abstract: In the paper we propose a novel multiple kernel learning framework for representation-based classification (MKL-RC) of remote sensing image scenes. Unlike the existing methods that often greedily learn an optimal combined kernel from predefined base kernels by optimization method, resulting in high computation time but relatively better performance. The proposed approach is different from traditional kernel methods and characterized by multiple feature and multiple kernel learning in a representation-based classification manner. Experimental results on two real remote sensing scene datasets demonstrate that the proposed methods can achieve superior performance than the state-of-the-art classification methods.

Multiple kernel learning using single stage function approximation for binary classification problems

Abstract: In this paper, the multiple kernel learning (MKL) is formulated as a supervised classification problem. We dealt with binary classification data and hence the data modelling problem involves the computation of two decision boundaries of which one related with that of kernel learning and the other with that of input data. In our approach, they are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS) as the direct sum of the RKHSs corresponding to the decision boundaries of kernel learning and input data and searching that function from the global RKHS, which can be represented as the direct sum of the decision boundaries under consideration. In our experimental analysis, the proposed model had shown superior performance in comparison with that of existing two stage function approximation formulation of MKL, where the decision functions of kernel learning and input data are found separately using two different cost functions. This is due to th...

Kernel Selection using Multiple Kernel Learning and Domain Adaptation in Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance Scenario.

Abstract: Face Recognition (FR) has been the interest to several researchers over the past few decades due to its passive nature of biometric authentication. Despite high accuracy achieved by face recognition algorithms under controlled conditions, achieving the same performance for face images obtained in surveillance scenarios, is a major hurdle. Some attempts have been made to super-resolve the low-resolution face images and improve the contrast, without considerable degree of success. The proposed technique in this paper tries to cope with the very low resolution and low contrast face images obtained from surveillance cameras, for FR under surveillance conditions. For Support Vector Machine classification, the selection of appropriate kernel has been a widely discussed issue in the research community. In this paper, we propose a novel kernel selection technique termed as MFKL (Multi-Feature Kernel Learning) to obtain the best feature-kernel pairing. Our proposed technique employs a effective kernel selection by Multiple Kernel Learning (MKL) method, to choose the optimal kernel to be used along with unsupervised domain adaptation method in the Reproducing Kernel Hilbert Space (RKHS), for a solution to the problem. Rigorous experimentation has been performed on three real-world surveillance face datasets : FR\_SURV, SCface and ChokePoint. Results have been shown using Rank-1 Recognition Accuracy, ROC and CMC measures. Our proposed method outperforms all other recent state-of-the-art techniques by a considerable margin.

Drift Compensation for E-Nose Using QPSO-Based Domain Adaptation Kernel ELM

Abstract: A novel theoretical framework for drift compensation and classification of an electronic nose (E-nose), called QPSO-based domain adaptation kernel extreme learning machine (QDA-KELM) is presented in the work. The kernel method combines with domain adaption extreme learning machine (DAELM) to remove the drift in E-nose and enhance the classification performance. A swarm intelligent algorithm is utilized for the optimization of the model parameters. In order to evaluate the performance of our approach, three types of common kernels are used to form the composite kernel function. In addition, ELM and DAELM are compared with the proposed method. Finally, we also applied Analysis of Variance (ANOVA) to demonstrate our results are significantly better than the control methods.