Deep kernel learning in core vector machines

Afzal, A. L.; Asharaf, S.

doi:10.1007/s10044-017-0600-4

Cited by 8 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each feature extraction layer mimics an unsupervised MKL producer. This novel framework uses the arc-cosine kernel [28], a multiple kernel form of the proposed algorithm [31].…”

Section: Introductionmentioning

confidence: 99%

A New Convolutional Kernel Classifier for Hyperspectral Image Classification

Ansari¹,

Homayouni²,

Safari³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Multiple Kernel Learning (MKL) algorithms are among the most successful classification methods for hyperspectral data. Nevertheless, these algorithms suffer from two main drawbacks of computational complexity and debility to admit to the end-to-end learning paradigm. This paper proposed a Convolutional Kernel Classifier (CKC) for hyperspectral remote sensing images to address these issues. The CKC uses the Nyström approximation method to estimate a low-rank approximation of the basis kernels, thus solves the issues associated with the high dimensionality of the basis kernels. The CKC uses deep architecture to learn the optimal combination of the basis kernels and the classification task to enable end-to-end learning. The proposed CKC's architecture is based on a 1D-Convolutional Neural Network (CNN-1D), and it uses kernel dropout to prevent overfitting. It is the first instance of deep-kernel algorithms in the field of remote sensing. The proposed method was compared with several well-known hyperspectral image analysis MKL algorithms, including a multi-kernel variant of the deep kernel machine optimization (M-DKMO), MKL-average, Simple-MKL, and Generalize MKL (GMKL), and state-of-the-art deep learning models, including Vanilla Recurrent Neural Network (VanillaRNN) and CNN-1D in classifying four benchmark hyperspectral datasets. The experimental results show that the CKC consistently outperforms all the competitor methods, and its runtime is lower than its MKL algorithm counterparts on four benchmark hyperspectral datasets. Moreover, the Nyström approximation solves the high dimensionality of the basis kernels and boosts classification accuracy. The source codes of CKC are available from: https://github.com/MohsenAnsari1373/A-New-Convolutional-Kernel-Classifier-for-Hyperspectral-Image-Classification .

show abstract

“…Each feature extraction layer mimics an unsupervised MKL producer. This novel framework uses the arc-cosine kernel [28], a multiple kernel form of the proposed algorithm [31].…”

Section: Introductionmentioning

confidence: 99%

A New Convolutional Kernel Classifier for Hyperspectral Image Classification

Ansari¹,

Homayouni²,

Safari³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The machine learning methods are divided into unsupervised and supervised learning. The unsupervised learning includes multidimensional scaling, NMF, ICA, neighborhood preserving embedding, Locality Preserving Projection (LPP) [1], and other computing methods [2], and for the supervised learning, generalized discriminant analysis [3], uncorrelated discriminant vector analysis [4], and some acceleration algorithm [5,6]. In recent years, the kernel-based machine learning algorithms were presented for the feature extraction; this paper proposes an improved kernel function supervised kernel-based LPP, local structure supervised feature extraction [7], kernel subspace LDA [8], kernel MSE [9], and quasiconformal mapping-based kernel machine [10].…”

Section: Introductionmentioning

confidence: 99%

Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval

Liu

Qiao

2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Intelligent internet data mining is an important application of AIoT (Artificial Intelligence of Things), and it is necessary to construct large training samples with the data from the internet, including images, videos, and other information. Among them, a hyperspectral database is also necessary for image processing and machine learning. The internet environment provides abundant hyperspectral data resources, but the hyperspectral data have no class labels and no so high value for applications. So, it is important to label the class information for these hyperspectral data through machine learning-based classification. In this paper, we present a quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm for internet-based hyperspectral data retrieval. The contributions include three points: the quasiconformal mapping-based multiple kernel learning network framework is proposed for hyperspectral data classification, the Mahalanobis distance kernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function learning, and the objective function of measuring the class discriminative ability is proposed to seek the optimal parameters of the quasiconformal mapping projection. Experiments show that the proposed scheme is effective for hyperspectral image classification and retrieval.

show abstract

“…Typical unsupervised metric learning includes multidimensional scaling, nonnegative matrix factorization (NMF), independent component analysis (ICA), neighborhood preserving embedding, locality preserving projection (LPP), 1 and other computing methods. 2,3 In previous works, many researchers have developed dimensionality reduction methods for different application fields, such as generalized discriminant analysis, 4 uncorrelated discriminant vector analysis (a criterion for optimizing kernel parameters), 5 and kernel machine-based one-parameter regularized Fisher discriminant 6,7 methods. In addition, other recognition algorithms have been applied in other application areas, such as vehicle estimation.…”

Section: Introductionmentioning

confidence: 99%

Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing

Liu

Qiao

2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Spectral dimensionality reduction is a crucial step for hyperspectral image classification in practical applications. Dimensionality reduction has a strong influence on image classification performance with the problems of strong coupling features and high band correlation. To solve these issues, we propose the Mahalanobis distance–based kernel supervised machine learning framework for spectral dimensionality reduction. With Mahalanobis distance matrix–based dimensional reduction, the coupling relationship between features and the elimination of the scale effect are removed in low-dimensional feature space, which benefits the image classification. The experimental results show that compared with other methods, the proposed algorithm demonstrates the best accuracy and efficiency. The Mahalanobis distance–based multiples kernel learning achieves higher classification accuracy than the Euclidean distance kernel function. Accordingly, the proposed Mahalanobis distance–based kernel supervised machine learning method performs well with respect to the spectral dimensionality reduction in hyperspectral imaging remote sensing.

show abstract

Deep kernel learning in core vector machines

Cited by 8 publications

References 13 publications

A New Convolutional Kernel Classifier for Hyperspectral Image Classification

A New Convolutional Kernel Classifier for Hyperspectral Image Classification

Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval

Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing

Contact Info

Product

Resources

About