Topic
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.
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10 Jul 2016TL;DR: This paper demonstrates how extracting high-level features from both the 2D orthomosaic as well as the 3D point cloud (obtained by an UAV), and integrating them through a MKL approach, can obtain an Overall Accuracy of 90.29%, a 4% increase over the results obtained using single kernel methods.
Abstract: Informal settlement upgrading projects require high-resolution and up-to-date thematic maps in order to plan and design effective interventions. To this end, Unmanned Aerial Vehicles (UAVs) provide the opportunity to obtain very high resolution 2D orthomosaics and 3D point clouds where and when needed. The heterogeneous, dense structures which typically make up an informal settlement motivate the importance of integrating complex 2D and 3D features obtained from UAV data into a single classification problem. Multiple Kernel Learning (MKL) Support Vector Machines (SVMs) maintain the distinct characteristics of the different feature spaces by optimizing individual kernels for specific feature groups which are later combined into a single kernel used for classification. Both the kernel parameters and kernel weights can be optimized by considering the alignment between the kernel and an ideal kernel which would perfectly classify the samples. This paper demonstrates how extracting high-level features from both the 2D orthomosaic as well as the 3D point cloud (obtained by an UAV), and integrating them through a MKL approach, can obtain an Overall Accuracy of 90.29%, a 4% increase over the results obtained using single kernel methods.
3 citations
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18 Apr 2019TL;DR: In this paper, an extension of the kernel activation function (KAF) is proposed, in which multiple kernels are linearly combined at every neuron, inspired by the literature on multiple kernel learning.
Abstract: The design of activation functions is a growing research area in the field of neural networks. In particular, instead of using fixed point-wise functions (e.g., the rectified linear unit), several authors have proposed ways of learning these functions directly from the data in a non-parametric fashion. In this paper we focus on the kernel activation function (KAF), a recently proposed framework wherein each function is modeled as a one-dimensional kernel model, whose weights are adapted through standard backpropagation-based optimization. One drawback of KAFs is the need to select a single kernel function and its eventual hyper-parameters. To partially overcome this problem, we motivate an extension of the KAF model, in which multiple kernels are linearly combined at every neuron, inspired by the literature on multiple kernel learning. We provide an application of the resulting multi-KAF on a realistic use case, specifically handwritten Latin OCR, on a large dataset collected in the context of the ‘In Codice Ratio’ project. Results show that multi-KAFs can improve the accuracy of the convolutional networks previously developed for the task, with faster convergence, even with a smaller number of overall parameters.
3 citations
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TL;DR: A new diagnosis algorithm of PD is proposed by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy.
3 citations
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TL;DR: Experimental evaluations in the well-known four-class MI dataset 2a BCI competition IV proves that the methodology significantly improves state-of-the-art approaches and interpretability demonstrates that MKSSP encodes features from different spectral bands into a single representation improving the discrimination of mental tasks.
Abstract: Brain–computer interface (BCI) systems communicate the human brain and computers by converting electrical activity into commands to use external devices. Such kind of system has become an alternative for interaction with the environment for people suffering from motor disabilities through the motor imagery (MI) paradigm. Despite being the most widespread, electroencephalography (EEG)-based MI systems are highly sensitive to noise and artifacts. Further, spatially close brain activity sources and variability among subjects hampers the system performance. This work proposes a methodology for the classification of EEG signals, termed Multiple Kernel Stein Spatial Patterns (MKSSP) dealing with noise, raveled brain activity, and subject variability issues. Firstly, a bank of bandpass filters decomposes brain activity into spectrally independent multichannel signals. Then, Multi-Kernel Stein Spatial Patterns (MKSSP) maps each signal into low-dimensional covariance matrices preserving the nonlinear channel relationships. The Stein kernel provides a parameterized similarity metric for covariance matrices that belong to a Riemannian manifold. Lastly, the multiple kernel learning assembles the similarities from each spectral decomposition into a single kernel matrix that feeds the classifier. Experimental evaluations in the well-known four-class MI dataset 2a BCI competition IV proves that the methodology significantly improves state-of-the-art approaches. Further, the proposal is interpretable in terms of data distribution, spectral relevance, and spatial patterns. Such interpretability demonstrates that MKSSP encodes features from different spectral bands into a single representation improving the discrimination of mental tasks.
3 citations
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TL;DR: This paper addresses the problem of combining multi-modal kernels in situations in which object correspondence information is unavailable between modalities, for instance, where missing feature values exist, or when using proprietary databases in multi- modal biometrics.
3 citations