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.

Identification of emergent leaders in a meeting scenario using multiple kernel learning

Abstract: In this paper, an effective framework for detection of emergent leaders in small group is presented. In this scope, the combination of different types of nonverbal visual features; the visual focus of attention, head activity and body activity based features are utilized. Using them together ensued significant results. For the first time, multiple kernel learning (MKL) was applied for the identification of the most and the least emergent leaders. Taking the advantage of MKL's capability to use different kernels which corresponds to different feature subsets having different notions of similarity, significantly improved results compared to the state of the art methods were obtained. Additionally, high correlations between the majority of the features and the social psychology questionnaires which are designed to estimate the leadership or dominance were demonstrated.

Multiple kernel support vector regression for economic forecasting

Abstract: Economic forecasting has become an important research topic in field of management science. Economic operation is a complex and changeable thing. There are many factors, which impact development of economy positively or negatively. This fact makes the economic system have dynamic, non-linear and uncertain characteristics. In this paper, a forecasting method is proposed for economic research, based on multiple kernel support vector regression. In the proposed method, we provide the forecasting framework for economy by means of multiple kernel support vector regression and multiple kernel learning mechanism. To validate the effectiveness of the proposed method, experiments are conducted on total production amount data from Chinese first and second industry. The numerical result shows that the proposed method greatly outperform conventional BP neural network and support vector machine with simple kernel in terms of forecasting performance.

Deterministic Multi-kernel based extreme learning machine for pattern classification

Abstract: The Extreme learning machine (ELM) designed by Huang et al. is proved to be a fast and good classifier over a decade, but existing ELM is non-deterministic in nature as well as kernel dependent and needs attention to optimize the selection of kernels. In ELM feature space is obtained with the help of single kernel function. The choice of kernel depends on perceptiveness of classification problem. So a generalized framework with deterministic nature along with optimized kernel is ought to be designed that can be applied to large domain of real world heterogeneous pattern classification problems. This paper presents a deterministic extreme learning machine for neural network with feedforward architecture which is formulated with multiple kernel learning. We further enhance this approach by incorporating Gray level co-occurrence matrix (GLCM) for multi-modal feature extraction. Two formulation of kernel extreme learning machine are introduced, with target kernel function as a linear combination of different base kernels. The first one is based on deterministic multiple kernel learning while the second one uses deterministic multiple kernel learning along with GLCM for extracting the invariant feature vectors. The performance of proposed algorithms are analyzed on pattern recognition problem for face recognition by changing the number of training set, types of kernel used and coefficients used for combining base kernels. The superior recognition rate is achieved for prominent multi-class face databases, when compared with contemporary methods that prove the efficacy of proposed algorithms.

Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation

Abstract: As a fundamental constituent of machine learning, domain adaptation generalizes a learning model from a source domain to a different (but related) target domain. In this paper, we focus on semi-supervised domain adaptation and explicitly extend the applied range of unlabeled target samples into the combination of distribution alignment and adaptive classifier learning. Specifically, our extension formulates the following aspects in a single optimization: 1) learning a cross-domain predictive model by developing the Fredholm integral based kernel prediction framework; 2) reducing the distribution difference between two domains; 3) exploring multiple kernels to induce an optimal learning space. Correspondingly, such an extension is distinguished with allowing for noise resiliency, facilitating knowledge transfer and analyzing diverse data characteristics. It is emphasized that we prove the differentiability of our formulation and present an effective optimization procedure based on the reduced gradient, guaranteeing rapid convergence. Comprehensive empirical studies verify the effectiveness of the proposed method.