Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning

Thiagarajan, Jayaraman J.; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas

doi:10.1109/tip.2014.2322938

Cited by 67 publications

(31 citation statements)

References 53 publications

(66 reference statements)

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“…The curves in Figs. 15-17 show the most important features in LiDAR point cloud data classification, which include the relative height (169), the variance of intensity of different neighborhoods (49)(50)(51)(52)(53)(54), and the vertical angle of spherical neighborhood (80-84).…”

Section: Classificationmentioning

confidence: 99%

Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification

Wang

Xie³

2017

IEEE Trans. Geosci. Remote Sensing

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To effectively learn heterogeneous features extracted from raw LiDAR point cloud data for landcover classification, a multiple kernel sparse representation classification (MKSRC) framework is proposed in this paper. In the MKSRC, multiple kernel learning (MKL) is embedded into sparse representation classification (SRC). The heterogeneous features are first extracted from the raw LiDAR point cloud data before classification. These features contain useful information from different dimensions, including single point features and neighbor features. Based on feature extraction, on the one hand, MKL is reasonably integrated into the SRC, namely, different base kernels that are constructed with each heterogeneous feature separately are utilized in the process of sparse representation. Furthermore, joint sparsity model is also introduced into the MKSRC framework and multiple kernel joint SRC (MKJSRC) is then proposed. On the other hand, improved kernel alignment (IKA) methods are proposed to more effectively determine the weights of base kernels in both of MKSRC and MKJSRC.Experiments are conducted on three real airborne LiDAR data sets. The experimental results demonstrate that MKSRC and MKJSRC frameworks can effectively learn the heterogeneous features for LiDAR point cloud classification and outperforms the other state-of-the-art sparse representation-based classifiers and the recent MKL algorithm. Moreover, the proposed IKA is helpful to better determine the "optimal" weights of the base kernels in both MKSRC and MKJSRC than in the existing kernel alignment method.

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Section: Classificationmentioning

confidence: 99%

Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification

Wang

Xie³

2017

IEEE Trans. Geosci. Remote Sensing

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“…Common shading and fault conditions will be safely generated in order to build a comprehensive dataset for designing and evaluating monitoring techniques. The use of statistical signal processing algorithms [1,17,18,21], imaging techniques [19] for forecasting solar irradiance [27], and machine learning [20,24,26,28,29].…”

Section: Design Of the Solar Array Research Facilitymentioning

confidence: 99%

An 18 kW solar array research facility for fault detection experiments

Rao

Ramirez

Braun

et al. 2016

2016 18th Mediterranean Electrotechnical Conference (MELECON)

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Monitoring utility-scale solar arrays was shown to minimize cost of maintenance and help optimize the performance of the array under various conditions. In this paper, we describe the design of an 18 kW experimental facility that consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. Research planned at this stage includes developing machine learning methods for fault detection. Preliminary simulation results on fault detection using machine learning are given in this paper.

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“…While the kernel sparse representation has been extensively developed, most algorithms [33], [34], [35] are still primarily developed for data points lying on the vector space. In this work, we focus on the sparse representation of SPD matrices, Sym + d .…”

Section: B Kernel Sparse Representationmentioning

confidence: 99%

Manifold Kernel Sparse Representation of Symmetric Positive-Definite Matrices and Its Applications

Jia

et al. 2015

IEEE Trans. on Image Process.

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The symmetric positive-definite (SPD) matrix, as a connected Riemannian manifold, has become increasingly popular for encoding image information. Most existing sparse models are still primarily developed in the Euclidean space. They do not consider the non-linear geometrical structure of the data space, and thus are not directly applicable to the Riemannian manifold. In this paper, we propose a novel sparse representation method of SPD matrices in the data-dependent manifold kernel space. The graph Laplacian is incorporated into the kernel space to better reflect the underlying geometry of SPD matrices. Under the proposed framework, we design two different positive definite kernel functions that can be readily transformed to the corresponding manifold kernels. The sparse representation obtained has more discriminating power. Extensive experimental results demonstrate good performance of manifold kernel sparse codes in image classification, face recognition, and visual tracking.

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Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning

Cited by 67 publications

References 53 publications

Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification

Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification

An 18 kW solar array research facility for fault detection experiments

Manifold Kernel Sparse Representation of Symmetric Positive-Definite Matrices and Its Applications

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