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Supervised Learning via Unsupervised sparse autoencoder

IEEE ACCESS 2018年 6卷 73802-73814页

作者： Liu, Jianran Li, Chan Yang, Wenyuan Minnan Normal Univ Fujian Key Lab Granular Comp & Applicat Zhangzhou 363000 Peoples R China Xiamen Univ Tan KahKee Coll Xiamen 363105 Peoples R China

Dimensionality reduction is commonly used to preprocess high-dimensional data, which is an essential step in machine learning and data mining. An outstanding low-dimensional feature can improve the efficiency of subsequent learning tasks. However, existing methods of dimensionality reduction mostly involve datasets with sufficient labels and fail to achieve effective feature vectors for datasets with insufficient labels. In this paper, an unsupervised multiple layered sparse autoencoder model is studied. Its advantage is that it reduces the reconstruction error as its optimization goal, with the resulting low-dimensional feature being reconstructed to the original dataset as much as possible. Therefore, the reduction of high-dimensional datasets to low-dimensional datasets is effective. First, the relationship among the reconstructed data, the number of iterations, and the number of hidden variables is explored. Second, the dimensionality reduction ability of the sparse autoencoder is proven. Several classical feature representation methods are compared with the sparse autoencoder on publicly available datasets, and the corresponding low-dimensional representations are placed into different supervised classifiers and the classification performances reported. Finally, by adjusting the parameters that might influence the classification performance, the parametric sensitivity of the sparse autoencoder is shown. The extensively low-dimensional feature classification experimental results demonstrated that the sparse autoencoder is more efficient and reliable than the other selected classical dimensional reduction algorithms.

关键词： Machine learning dimensionality reduction sparse autoencoder supervised learning feature representation

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Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第9期77卷 10521-10538页

作者： Zhang, Yu-Dong Zhang, Yin Hou, Xiao-Xia Chen, Hong Wang, Shui-Hua Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Hunan Prov Key Lab Network Invest Technol Changsha 410138 Hunan Peoples R China Nanjing Med Univ Dept Neurol Affiliated Hosp 1 Nanjing 210029 Jiangsu Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Hubei Peoples R China CUNY City Coll Dept Elect Engn New York NY 10031 USA

In order to detect the cerebral microbleed (CMB) voxels within brain, we used susceptibility weighted imaging to scan the subjects. Then, we used undersampling to solve the accuracy paradox caused from the imbalanced data between CMB voxels and non-CMB voxels. we developed a seven-layer deep neural network (DNN), which includes one input layer, four sparse autoencoder layers, one softmax layer, and one output layer. Our simulation showed this method achieved a sensitivity of 95.13%, a specificity of 93.33%, and an accuracy of 94.23%. The result is better than three state-of-the-art approaches.

关键词： Cerebral microbleed Deep neural network sparse autoencoder Voxelwise detection Accuracy paradox

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A hybrid Intrusion Detection System based on sparse autoencoder and Deep Neural Network

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COMPUTER COMMUNICATIONS 2021年 180卷 77-88页

作者： Rao, K. Narayana Rao, K. Venkata Reddy, P. V. G. D. Prasad Andhra Univ Coll Engn A Dept Comp Sci & Syst Engn Visakhapatnam AP India

A large number of attacks are launched daily in the era of the internet and with a large number of users. Nowadays, effective detection of numerous attacks using the Intrusion Detection System (IDS) is an emerging research technique. Machine learning methodologies show effective results in intrusion detection system. We proposed a two-stage hybrid methodology for intrusion detection. In the first stage, the unsupervised sparse autoencoder (SAE) with smoothed l1 regularization. We employ smoothed l1 regularization to enforce a sparsity of autoencoder. The smoothed l1 regularization is indeed able to learn sparse representations of features. In the second stage, the Deep Neural Network (DNN) was used to predict and classify attacks. The classifier classifies multi attack classification from the extracted features. Unsupervised SAE was optimized to train an efficient model. The experimental results demonstrate that proposed model better than the conventional models in terms of overall performance in detection rate and low false positive rate. The proposed model was assessed on the datasets KDDCup99, NSL-KDD and UNSW-NB15. The model attained the accuracy 99.98% , and detection rate 99.99% on UNSW-NB15 dataset.

关键词： Intrusion Detection sparse autoencoder Deep Neural Network Feature selection

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Post-fault prediction of transient instabilities using stacked sparse autoencoder

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ELECTRIC POWER SYSTEMS RESEARCH 2018年 164卷 243-252页

作者： Mahdi, Mohammed Genc, V. M. Istemihan Istanbul Tech Univ Dept Elect Engn TR-34469 Istanbul Turkey

Post-fault prediction of transient stability of power systems has a great impact on the performance of wide area monitoring, protection and control systems. Situational awareness capabilities of a power system are improved by fast detection of instabilities after severe fault occurrences. This allows sufficient time to take necessary corrective control actions. In this paper, a novel method based on stacked sparse autoencoder is proposed to predict the post-fault transient stability status of the power system directly after clearing the fault. A dataset is generated off-line to train a stacked sparse autoencoder, and then the trained stacked sparse autoencoder is used in an online application of predicting any transient instability. The stacked sparse autoencoder is fed by the inputs, which are specific points extracted from the fault-on voltage magnitude measurements collected from the phasor measurement units. The effectiveness of the proposed method is demonstrated and compared with the conventional approaches that adopt multilayer perceptrons or post-fault measurements as it is applied to the 127-bus WSCC test system and to the Turkish power system.

关键词： Deep learning Phasor measurement units Prediction sparse autoencoder Transient stability Wide area measurements

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Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked sparse autoencoder

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JOURNAL OF GRID COMPUTING 2022年第1期20卷 1-16页

作者： Wang, Shui-Hua Satapathy, Suresh Chandra Zhou, Qinghua Zhang, Xin Zhang, Yu-Dong Univ Leicester Sch Math & Actuarial Sci Leicester LE1 7RH Leics England KIIT Deemed Univ Sch Comp Eng Bhubaneswar India Univ Leicester Sch Informat Leicester LE1 7RH Leics England Fourth Peoples Hosp Huaian Dept Med Imaging Huaian 223002 Jiangsu Peoples R China

Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% +/- 1.47%, a specificity of 93.13% +/- 0.95%, a precision of 93.15% +/- 0.89%, an accuracy of 93.23% +/- 0.81%, and an F1 score of 93.23% +/- 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.

关键词： Secondary pulmonary tuberculosis pseudo-Zernike moment sparse autoencoder Machine learning Deep learning

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Fault diagnosis of planetary gear backlash based on motor current and Fisher criterion optimized sparse autoencoder

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第13期236卷 7529-7545页

作者： Zhang, Ziqin Wu, Xing Liu, Tao Liu, Xiaoqin Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming Yunnan Peoples R China Yunnan Vocat Coll Mech & Elect Technol Kunming Yunnan Peoples R China

Planetary gear reducer is widely applied in various transmission equipment, and its performance highly affects the operation of a machine. The appearance of unreasonable backlash in planetary gear reducer may lead to undesirable vibration, which may accelerate the degradation of equipment and eventually cause premature failure. In traditional condition-based monitoring (CBM), sensors such as accelerometers have been utilized to detect the fault of planetary gear. However, the complexity and integration of planetary gear limit the installation and application of vibration sensors in practice. In this case, the current signal from motor, as a convenient real-time monitoring approach, is introduced into the CBM of the planetary gear. In this paper, a fault diagnosis method based on drive motor current signal analysis (MCSA) is presented to identify the backlash fault in a planetary gear reducer. In this method, frequency domain data of the original current signal is found and used to automatically extract fault features. A deep sparse autoencoder (DSAE) extracts the required features. In particular, the Fisher criterion is introduced to evaluate the sensitivity of these features. It then selects the most effective ones for improving diagnostic accuracy as well as diagnostic efficiency. Experimental test data shows that under different load conditions, this method outperforms other typical fault diagnosis methods and exhibits the best performance.

关键词： fault diagnosis gear backlash planetary gear reducer motor current signal analysis sparse autoencoder Fisher criterion feature selection

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Preference-inspired coevolutionary algorithm with sparse autoencoder for many-objective optimization

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SOFT COMPUTING 2023年第23期27卷 17729-17745页

作者： Wang, Wei Zhang, Shanxin Song, Weida Ge, Wenlong Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China

The function of most multi-objective algorithms (MOEAs) is to provide an overall trade-off Pareto front to the decision makers (DMs). But DMs actually tend to have a preference for a specific subset of the Pareto front. So combing decision makers' preference information with many-objective optimization methods has recently been a hot survey topic in the research field of many-objective optimization. The preference-inspired coevolutionary algorithms using goal vectors (PICEA-g) coevolve a family of decision makers' preferences called preference set together with a population of candidate solutions. It is a classic and effective algorithm, but it has shortcomings in distribution and there is a waste of computing resources. So we use a sparse autoencoder as a controller to perform feature processing on the target solution set in the process of coordinated evolution and modify the fitness assignment formula to improve the diversity. The combination of PICEA-g and sparse autoencoder framework enhance the convergence and expand the ability of the PICEA-g algorithm. The preference-inspired coevolutionary algorithm with sparse autoencoder (PICEA-g/SAE) is evaluated by three widely used benchmark suites and compared with nine classic multi-objective evolutionary algorithms to prove the advantages of the sparse autoencoder framework. The experimental results show that PICEA-g/SAE could have a good performance on many DTLZ test problems. Moreover, PICEA-g/SAE on UF1-9 and WFG1-9 test problems in low dimensions could have good convergence, diversity, and spread compared with other algorithms.

关键词： Many-objective optimization Preference-inspired coevolutionary algorithms sparse autoencoder Decision makers

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SARS-CoV-2 virus classification based on stacked sparse autoencoder

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2023年 21卷 284-298页

作者： Coutinho, Maria G. F. Camara, Gabriel B. M. Barbosa, Raquel de M. Fernandes, Marcelo A. C. Univ Fed Rio Grande do Norte Lab Machine Learning & Intelligent Instrumentat IMD nPITI Natal Brazil Univ Granada Dept Pharm & Pharmaceut Technol Granada 18071 Spain Univ Fed Rio Grande do Norte Dept Comp & Automat Engn Natal Brazil Univ Fed Rio Grande do Norte Dept Comp & Automat Engn Natal Brazil

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. Considering that motivation, the authors proposed an efficient viral genome classifier for the SARS-CoV-2 using the deep neural net-work based on the stacked sparse autoencoder (SSAE). For the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation was applied. We performed four experiments to provide different levels of taxonomic classification of the SARS-CoV-2. The SSAE technique provided great performance results in all experiments, achieving classification accuracy between 92% and 100% for the validation set and between 98.9% and 100% when the SARS-CoV-2 samples were applied for the test set. In this work, samples of the SARS-CoV-2 were not used during the training process, only during subsequent tests, in which the model was able to infer the correct classification of the samples in the vast majority of cases. This indicates that our model can be adapted to classify other emerging viruses. Finally, the results indicated the applicability of this deep learning technique in genome classification problems.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

关键词： COVID-19 Deep learning SARS-CoV-2 sparse autoencoder Viral classification

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Hierarchical fusion network for periocular and iris by neural network approximation and sparse autoencoder

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MACHINE VISION AND APPLICATIONS 2020年第1期32卷 p1-10页

作者： Algashaam, Faisal Kien Nguyen Banks, Jasmine Chandran, Vinod Tuan-Anh Do Alkanhal, Mohamed Queensland Univ Technol Brisbane Qld Australia King Abdulaziz City Sci & Technol Riyadh Saudi Arabia Hanoi Univ Sci & Technol Hanoi Vietnam

The eye region is one of the most attractive sources for identification and verification due to the representative availability of such biometric modalities as periocular and iris. Many score-level fusion approaches have been proposed to combine these two modalities targeting to improve the robustness. The score-level approaches can be grouped into three categories: transformation-based, classification-based and density-based. Each category has its own benefits, if combined can lead to a robust fusion mechanism. In this paper, we propose a hierarchical fusion network to fuse multiple fusion approaches from transformation-based and classification-based categories into a unified framework for classification. The proposed hierarchical approach relies on the universal approximation theorem for neural networks to approximate each fusion approach as one child neural network and then ensemble them into a unified parent network. This mechanism takes advantage of both categories to improve the fusion performance, illustrated by an improved equal error rate of the multimodal biometric system. We subsequently force the parent network to learn the representation and interaction strategy between the child networks from the training data through a sparse autoencoder layer, leading to further improvements. Experiments on two public datasets (MBGC version 2 and CASIA-Iris-Thousand) and our own dataset validate the effectiveness of the proposed hierarchical fusion approach for periocular and iris modalities.

关键词： Periocular recognition Iris recognition Hierarchical multimodal fusion Neural network approximation sparse autoencoder

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EndNet: sparse autoencoder Network for Endmember Extraction and Hyperspectral Unmixing

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第1期57卷 482-496页

作者： Ozkan, Savas Kaya, Berk Akar, Gozde Bozdagi TUBITAK Space Technol Res Inst Image Proc Dept TR-06800 Ankara Turkey Middle East Tech Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey

Data acquired from multichannel sensors are a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors, and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions. Spectral unmixing is a technique that allows us to obtain the material spectral signatures and their fractions from hyperspectral data. In this paper, we propose a novel endmember extraction and hyperspectral unmixing scheme, so-called EndNet, that is based on a two-staged autoencoder network. This well-known structure is completely enhanced and restructured by introducing additional layers and a projection metric [i.e., spectral angle distance (SAD) instead of inner product] to achieve an optimum solution. Moreover, we present a novel loss function that is composed of a Kullback-Leibler divergence term with SAD similarity and additional penalty terms to improve the sparsity of the estimates. These modifications enable us to set the common properties of endmembers, such as nonlinearity and sparsity for autoencoder networks. Finally, due to the stochastic-gradient-based approach, the method is scalable for large-scale data and it can be accelerated on graphical processing units. To demonstrate the superiority of our proposed method, we conduct extensive experiments on several well-known data sets. The results confirm that the proposed method considerably improves the performance compared to the state-of-the-art techniques in the literature.

关键词： Endmember extraction hyperspectral unmixing sparse autoencoder

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