Self-Adaptive Deep Multiple Kernel Learning Based on Rademacher Complexity

Ren, Shengbing; Shen, Wangbo; Siddique, Chaudry Naeem; Li, You

doi:10.3390/sym11030325

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…Deep multiple kernel learning(DMKL) [ 12 – 15 , 20 – 24 ] is a hot research topic inspired by deep learning in recent years. This method explores the combination of multiple kernels in a multi-layer architecture and achieves success on various datasets.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 15 ], we propose an adaptive deep multiple kernel learning (SA-DMKL) method. It can optimize the model parameters of each kernel with the grid search method.…”

Section: Related Workmentioning

confidence: 99%

“…So, we propose an adaptive DMKL architecture [ 15 ] to solve the problem of a fixed number of layers. The growth of layers can be limited by setting a cutoff condition, and model layers can change with different training datasets.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, the classification performance is not the best, and wasting of computing power, needing more data, and so on. Therefore, in [ 15 ], we propose an adaptive deep multiple kernel learning framework, which solves the problem of the model with a fixed number of layers. Increasing the number of layers according to the actual datasets, and the cutoff condition for increasing the number of layers is that the highest classification accuracy is continuously unchanged for several layers.…”

Section: Introductionmentioning

confidence: 99%

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Group-based local adaptive deep multiple kernel learning with lp norm

Ren¹,

Fa²,

Zhou³

et al. 2020

PLoS ONE

Self Cite

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The deep multiple kernel Learning (DMKL) method has attracted wide attention due to its better classification performance than shallow multiple kernel learning. However, the existing DMKL methods are hard to find suitable global model parameters to improve classification accuracy in numerous datasets and do not take into account inter-class correlation and intra-class diversity. In this paper, we present a group-based local adaptive deep multiple kernel learning (GLDMKL) method with lp norm. Our GLDMKL method can divide samples into multiple groups according to the multiple kernel k-means clustering algorithm. The learning process in each well-grouped local space is exactly adaptive deep multiple kernel learning. And our structure is adaptive, so there is no fixed number of layers. The learning model in each group is trained independently, so the number of layers of the learning model maybe different. In each local space, adapting the model by optimizing the SVM model parameter α and the local kernel weight β in turn and changing the proportion of the base kernel of the combined kernel in each layer by the local kernel weight, and the local kernel weight is constrained by the lp norm to avoid the sparsity of basic kernel. The hyperparameters of the kernel are optimized by the grid search method. Experiments on UCI and Caltech 256 datasets demonstrate that the proposed method is more accurate in classification accuracy than other deep multiple kernel learning methods, especially for datasets with relatively complex data.

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Section: Related Workmentioning

confidence: 99%

“…In [ 15 ], we propose an adaptive deep multiple kernel learning (SA-DMKL) method. It can optimize the model parameters of each kernel with the grid search method.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Group-based local adaptive deep multiple kernel learning with lp norm

Ren¹,

Fa²,

Zhou³

et al. 2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

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“…Some authors assumed that the traditional MKL methods are not robust enough to cope with complex problems because they find the composite kernel from one layer of kernels. Accordingly, in [34][35][36], the authors proposed novel learning paradigms by extending the single layer MKL to multilayer MKL as Deep MKL (DMKL). In this way, in [34], a selfadaptive DMKL (SA-DMKL) method is proposed.…”

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

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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 .

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