Large-scale image classification using fast SVM with deep quasi-linear kernel

Liang, Peifeng; Li, Weite; Liu, Donghang; Hu, Jinglu

doi:10.1109/ijcnn.2017.7965970

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Qin-Qin Tao [30] proposed a locality-sensitive support vector machine using kernel combination (LS-KC-SVM) algorithm, which solved the large appearance variations due to some real-world factors on face detection. Liang [31] proposed an SVM-based method combining with the deep quasilinear kernel (DQLK) learning for large-scale image classification. It could train SVM on a large-scale dataset with less memory space and less training time.…”

Section: Kernel Methodmentioning

confidence: 99%

An Automatic Source Code Vulnerability Detection Approach Based on KELM

Tang

Yang²,

Ren³

et al. 2021

Security and Communication Networks

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Traditional vulnerability detection mostly ran on rules or source code similarity with manually defined vulnerability features. In fact, these vulnerability rules or features are difficult to be defined accurately, which usually cost much expert labor and perform weakly in practical applications. To mitigate this issue, researchers introduced neural networks to automatically extract features to improve the intelligence of vulnerability detection. Bidirectional Long Short-term Memory (Bi-LSTM) network has proved a success for software vulnerability detection. However, due to complex context information processing and iterative training mechanism, training cost is heavy for Bi-LSTM. To effectively improve the training efficiency, we proposed to use Extreme Learning Machine (ELM). The training process of ELM is noniterative, so the network training can converge quickly. As ELM usually shows weak precision performance because of its simple network structure, we introduce the kernel method. In the preprocessing of this framework, we introduce doc2vec for vector representation and multilevel symbolization for program symbolization. Experimental results show that doc2vec vector representation brings faster training and better generalizing performance than word2vec. ELM converges much quickly than Bi-LSTM, and the kernel method can effectively improve the precision of ELM while ensuring training efficiency.

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Section: Kernel Methodmentioning

confidence: 99%

An Automatic Source Code Vulnerability Detection Approach Based on KELM

Tang

Yang²,

Ren³

et al. 2021

Security and Communication Networks

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“…For high‐dimensional datasets, we pretrain a multilayer neural network or winer‐take‐all autoencoder (unsupervised learning) . For image datasets, we use a pretrained CNN with fully connected layers (transfer learning) . The pretrained NN is only used to construct a kernel for the SVM, and it is typically obtained by using unsupervised learning or transfer learning .…”

Section: Svm With Quasi‐linear Kernelmentioning

confidence: 99%

Fast SVM training using data reconstruction for classification of very large datasets

Liang

2019

IEEJ Transactions Elec Engng

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This paper proposes a fast support vector machine (SVM) training method for the classification of very large datasets using data reconstruction. The idea is to scale down the training data by removing the samples that have low probability to become support vectors (SVs) in the feature space. For this purpose, it applies a series of gradually refined rough SVM classifiers with a quasi-linear kernel to build rough separation boundaries and remove those samples that are far away from the boundary. In order to make the proposed algorithm efficient for both low-dimensional and high-dimensional datasets, efforts are made on three aspects. The first one is to compose a quasi-linear kernel using the information of data manifold and potential separation boundary such that the samples mapped to feature space keep a sparse distribution, especially in the direction perpendicular to the separation boundary. The second one is to avoid computing Euclidean distances between samples, which may lose its effect on very high dimensional datasets when mapping the samples to feature space and selecting the samples for training data reconstruction. The third one is to design a sophisticated iterative algorithm to gradually refine the rough SVM classifier so as to remove non-SVs efficiently. The proposed fast SVM training method is applied to different real-world large datasets and compared with different methods, and simulation results confirm the effectiveness of the proposed method, especially for very high dimensional datasets.

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“…For high‐dimensional datasets, we pretrain a multilayer winer‐take‐all autoencoder (unsupervised learning) . For image datasets, we use a pretrained CNN with fully connected layers (trainsfer learning) . The pretrained NN is only used to construct a kernel for SVM, and it is typically obtained by using unsupervised learning or transfer learning.…”

Section: Svm With a Quasi‐linear Kernelmentioning

confidence: 99%

Oversampling the minority class in a multi‐linear feature space for imbalanced data classification

Liang

2018

IEEJ Transactions Elec Engng

Self Cite

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This paper proposes a novel oversampling method for imbalanced data classification, in which the minority class samples are synthesized in a feature space to avoid the generated minority samples falling into the majority class regions. For this purpose, it introduces a multi-linear feature space (MLFS) based on a quasi-linear kernel, which is composed from a pretrained neural network (NN). By using the quasi-linear kernel, the proposed MLFS oversampling method avoids computing directly the Euclidean distances among the samples when oversampling the minority class and mapping the samples to high-dimensional feature space, which makes it easy to be applied to classification of high-dimensional datasets. On the other hand, by using kernel learning instead of representation learning using the NN, it makes an unsupervised learning, even a transfer learning, to be easily employed for the pretraining of NNs because a kernel is usually less dependent on a specific problem, which makes it possible to avoid considering the imbalance problem at the stage of pretraining the NN. Finally, a method is developed to oversample the synthetic minority samples by computing the quasi-linear kernel matrix instead of computing very high dimensional MLFS feature vectors directly. The proposed MLFS oversampling method is applied to different real-world datasets including image dataset, and simulation results confirm the effectiveness of the proposed method.

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Large-scale image classification using fast SVM with deep quasi-linear kernel

Cited by 6 publications

References 12 publications

An Automatic Source Code Vulnerability Detection Approach Based on KELM

An Automatic Source Code Vulnerability Detection Approach Based on KELM

Fast SVM training using data reconstruction for classification of very large datasets

Oversampling the minority class in a multi‐linear feature space for imbalanced data classification

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