Ensemble Multiple Kernel Active Learning For Classification of Multisource Remote Sensing Data

Zhang, Yuhang; Yang, Hsiuhan Lexie; Prasad, Saurabh; Pasolli, Edoardo; Jung, Jinha; Crawford, Melba M.

doi:10.1109/jstars.2014.2359136

Cited by 82 publications

(29 citation statements)

References 31 publications

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“…In recent works, MKL has been shown to outperform traditional single-kernel SVMs in many cases, especially for classification and feature fusion problems (Sonnenburg et al, 2006; Tian et al, 2012; Samek et al, 2013; Li et al, 2014; Zhang et al, 2015). In this paper, we employ MKL to infer information about electrode relevance by observing the kernel weights learned from training the machine for classification.…”

Section: Methodsmentioning

confidence: 99%

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Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals

et al. 2017

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With the development of Brain Machine Interface (BMI) systems, people with motor disabilities are able to control external devices to help them restore movement abilities. Longitudinal validation of these systems is critical not only to assess long-term performance reliability but also to investigate adaptations in electrocortical patterns due to learning to use the BMI system. In this paper, we decode the patterns of user's intended gait states (e.g., stop, walk, turn left, and turn right) from scalp electroencephalography (EEG) signals and simultaneously learn the relative importance of different brain areas by using the multiple kernel learning (MKL) algorithm. The region of importance (ROI) is identified during training the MKL for classification. The efficacy of the proposed method is validated by classifying different movement intentions from two subjects—an able-bodied and a spinal cord injury (SCI) subject. The preliminary results demonstrate that frontal and fronto-central regions are the most important regions for the tested subjects performing gait movements, which is consistent with the brain regions hypothesized to be involved in the control of lower-limb movements. However, we observed some regional changes comparing the able-bodied and the SCI subject. Moreover, in the longitudinal experiments, our findings exhibit the cortical plasticity triggered by the BMI use, as the classification accuracy and the weights for important regions—in sensor space—generally increased, as the user learned to control the exoskeleton for movement over multiple sessions.

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Section: Methodsmentioning

confidence: 99%

“…The penalty parameter was then selected by cross-validation in the range of [2 −1 , …, 2 15 ]. For further information of MKL experimental settings, we refer readers to Zhang et al (2015). All the experiments were implemented in Matlab R2014a using the SimpleMKL toolbox (Rakotomamonjy, 2008).…”

Section: Methodsmentioning

confidence: 99%

Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals

et al. 2017

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“…For instance, in [72], a composite kernel SVM was implemented for multisource DF; in [73], a composite kernel local Fisher's discriminant analysis was implemented (CK-LFDA) for multisource feature extraction in a kernel-induced space wherein a composite kernel feature space was constructed that optimally represented (in the sense of the local Fisher's ratio) multisource data; Zhang et al [74] provide a framework for multisource active learning using multikernel learning, etc. Likewise, statistical classifiers have been used for effective DF for remote sensing image analysis [75].…”

Section: Classificationmentioning

confidence: 99%

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

Mura

Prasad

Pacifici³

et al. 2015

Proc. IEEE

194

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International audience—Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR) and material content (multi and hyperspectral) of the objects in the image. Once considered together their comple-mentarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil-spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion) among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the Data Fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges

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“…ere is a significant amount of work in the literature for combining multiple kernels [15,16]. Various applications indicate that performance gains can be achieved by linear and nonlinear kernel combinations using MKL methods [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Multiple Kernel Learning Approach for Air Quality Prediction

Zheng

et al. 2018

Advances in Meteorology

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Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). e centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ.

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Ensemble Multiple Kernel Active Learning For Classification of Multisource Remote Sensing Data

Cited by 82 publications

References 31 publications

Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals

Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

A Multiple Kernel Learning Approach for Air Quality Prediction

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