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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.


Papers
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Journal ArticleDOI
TL;DR: HMKL is effective for the breast cancer evaluation and can be utilized by physicians to better understand the patient’s condition and it is proved that the Hadamard kernel is effective in HMKL.
Abstract: Breast cancer is one of the common kinds of cancer among women, and it ranks second among all cancers in terms of incidence, after lung cancer. Therefore, it is of great necessity to study the detection methods of breast cancer. Recent research has focused on using gene expression data to predict outcomes, and kernel methods have received a lot of attention regarding the cancer outcome evaluation. However, selecting the appropriate kernels and their parameters still needs further investigation. We utilized heterogeneous kernels from a specific kernel set including the Hadamard, RBF and linear kernels. The mixed coefficients of the heterogeneous kernel were computed by solving the standard convex quadratic programming problem of the quadratic constraints. The algorithm is named the heterogeneous multiple kernel learning (HMKL). Using the particle swarm optimization (PSO) in HMKL, we selected the kernel parameters, then we employed HMKL to perform the breast cancer outcome evaluation. By testing real-world microarray datasets, the HMKL method outperforms the methods of the random forest, decision tree, GA with Rotation Forest, BFA + RF, SVM and MKL. On one hand, HMKL is effective for the breast cancer evaluation and can be utilized by physicians to better understand the patient’s condition. On the other hand, HMKL can choose the function and parameters of the kernel. At the same time, this study proves that the Hadamard kernel is effective in HMKL. We hope that HMKL could be applied as a new method to more actual problems.

6 citations

Proceedings ArticleDOI
10 Jun 2016
TL;DR: A new architecture of FERT is presented, Emotion recognition of the FERT scheme employs Multiple Kernel Learning (MKL) framework which reportedly outperforms traditional classifiers, and a conceptually user centred e-learning model that has potentials for improving learning interaction is described.
Abstract: A Vision of most e-learning models is to accurately recognize learner's post (pre) learning feedbacks to improve learning interaction. Several effort towards user centred e-learning have been made in literature, but mostly concentrates on cognitive based feedbacks for learner's modelling. However, Beside cognitive factors, emotions of the learner are equally important but seldom neglected. This paper present a new architecture of FERT. A processing pipeline of the FERT is realized through results of a preliminary analysis across selected facial features descriptive techniques, namely, Gabor wavelet, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Further more, Emotion recognition of the FERT scheme employs Multiple Kernel Learning (MKL) framework which reportedly outperforms traditional classifiers. Experiments have been conducted on contextual emotion datasets and results shows good performances of the FERT scheme. Finally, a conceptually user centred e-learning model that has potentials for improving learning interaction is described.

6 citations

Posted Content
TL;DR: In this article, a robust multiple kernel learning algorithm was proposed to predict railway points failures in the Sydney Trains rail network, which is an extensive network of passenger and freight railways.
Abstract: Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Traditionally, maintenance of points is based on a fixed time interval or raised after the equipment failures. Instead, it would be of great value if we could forecast points' failures and take action beforehand, minimising any negative effect. To date, most of the existing prediction methods are either lab-based or relying on specially installed sensors which makes them infeasible for large-scale implementation. Besides, they often use data from only one source. We, therefore, explore a new way that integrates multi-source data which are ready to hand to fulfil this task. We conducted our case study based on Sydney Trains rail network which is an extensive network of passenger and freight railways. Unfortunately, the real-world data are usually incomplete due to various reasons, e.g., faults in the database, operational errors or transmission faults. Besides, railway points differ in their locations, types and some other properties, which means it is hard to use a unified model to predict their failures. Aiming at this challenging task, we firstly constructed a dataset from multiple sources and selected key features with the help of domain experts. In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. We present a robust multiple kernel learning algorithm for predicting points failures. Our model takes into account the missing pattern of data as well as the inherent variance on different sets of railway points. Extensive experiments demonstrate the superiority of our algorithm compared with other state-of-the-art methods.

6 citations

Journal ArticleDOI
TL;DR: A new framework and algorithm of trajectory space information based multiple kernel learning (TSI-MKL) is exploited, and a novel two-layer clustering is developed to cluster the obtained trajectories into several semantic groups and the ultimate video representation is generated by encoding each group.
Abstract: Action recognition in videos plays an important role in the field of computer vision and multimedia, and there exist lots of challenges due to the complexity of spatial and temporal information. Trajectory-based approach has shown to be efficient recently, and a new framework and algorithm of trajectory space information based multiple kernel learning (TSI-MKL) is exploited in this paper. First, dense trajectories are extracted as raw features, and three saliency maps are computed corresponding to color, space, and optical flow on frames at the same time. Secondly, a new method combining above saliency maps is proposed to filter the achieved trajectories, by which a set of salient trajectories only containing foreground motion regions is obtained. Afterwards, a novel two-layer clustering is developed to cluster the obtained trajectories into several semantic groups and the ultimate video representation is generated by encoding each group. Finally, representations of different semantic groups are fed into the proposed kernel function of a multiple kernel classifier. Experiments are conducted on three popular video action datasets and the results demonstrate that our presented approach performs competitively compared with the state-of-the-art.

6 citations

Proceedings ArticleDOI
Bo Sun1, Di Zhang1, Jun He1, Lejun Yu1, Xuewen Wu1 
21 Oct 2015
TL;DR: This work proposes a novel face detection and coarse alignment method, which is multi-feature fusion with Simple Multiple Kernel Learning1 (Simple-MKL) algorithm, and is contrasted with competitive and related algorithms, and demonstrated to achieve promising results.
Abstract: Face detection and alignment are two crucial tasks to face recognition which is a hot topic in the field of defense and security, whatever for the safety of social public, personal property as well as information and communication security. Common approaches toward the treatment of these tasks in recent years are often of three types: template matching-based, knowledge-based and machine learning-based, which are always separate-step, high computation cost or fragile robust. After deep analysis on a great deal of Chinese face images without hats, we propose a novel face detection and coarse alignment method, which is inspired by those three types of methods. It is multi-feature fusion with Simple Multiple Kernel Learning 1 (Simple-MKL) algorithm. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve promising results.

6 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202321
202244
202172
2020101
2019113
2018114