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K-Means Clustering Optimizing Deep Stacked sparse autoencoder

SENSING AND IMAGING 2019年第1期20卷 6-6页

作者： Bi, Yandong Wang, Peng Guo, Xuchao Wang, Zhijun Cheng, Shuhan Shandong Agr Univ Coll Informat Sci & Engn Tai An 271018 Shandong Peoples R China

Because of the large structure and long training time, the development cycle of the common depth model is prolonged. How to speed up training is a problem deserving of study. In order to accelerate training, K-means clustering optimizing deep stacked sparse autoencoder (K-means sparse SAE) is presented in this paper. First, the input features are divided into K small subsets by K-means clustering, then each subset is input into corresponding autoencoder model for training, which only has fewer nodes in the hidden layer than traditional models. After training, each autoencoder's trained weights and biases is merged to obtain the next layer's input features by feedforward network. The above steps are repeated till the softmax layer, then fine-tuning is carried out. Using MNIST-Rotation datasets to train the network that has three hidden layers and each layer has 800 nodes, the improved model has higher classification accuracy and shorter training time when K=10. With K increasing, the training time is reduced to almost the same as the fine-tuning time but the recognition ability is descended. Compared with the recently stacked denoising sparse autoencoder, the recognition accuracy is improved by 1%, not only the noise factor is not selected but also the training speed is significantly increased. The trained filters from the improved model is also used to train convolutional autoencoder, and it performs better than traditional models. We find that pre-training stage doesn't need large samples simultaneously, and small samples parallel training reduces the probability of falling into the local minimum.

关键词： K-means clustering sparse autoencoder Convolutional autoencoder Training method

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A sparse autoencoder Based Denosing the Spectrum Signal in LIBS

A Sparse Autoencoder Based Denosing the Spectrum Signal in L...

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第30届中国控制与决策会议

作者： Shibing Ye Zhixing Niu Peng Yang Junqing Sun Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology

Based on laser induced breakdown spectroscopy(LIBS) technique, the content of the main elements in the liquid steel of carbon steel alloy can be detected in real time during melting process. In order to detect the liquid alloy steel and forecast the content of the main elements in the alloy steel more accurately, we use the method of sparse autoencoder to reduce noise in the spectral data which were collected in the detecting process. The experimental results show that the sparse autoencoder method can effectively improve the accuracy and precision of the spectral data.

关键词： LIBS sparse autoencoder spectral data denosing

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Fault Diagnosis Based on Batch-normalized Stacked sparse autoencoder

Fault Diagnosis Based on Batch-normalized Stacked Sparse Aut...

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第三十九届中国控制会议

作者： Liu Xiaozhi Gao Yang Yang Yinghua College of Information Science and Engineering Northeastern University

A fault diagnosis method based on batch-normalization stacked sparse autoencoder(SSAE) is presented in this paper. This paper use the autoencoder to extract features for fault diagnosis on account of its good performance in feature extraction. In order to improve the accuracy of the extracted features, this paper use a sparse representation which is a constraint during the encoding process. The multi-layer structure of autoencoder has an internal covariate shift problem and the generalization ability of the network is critical, batch normalization is employed before the activation function in each layer of the autoencoder network. And a stacked method is utilized to optimize network structure and reduce training difficulty. So the features of the original signal are extracted by the network based on the above method and the extracted features are placed in the classifier to identify different health states. For the purpose of fault diagnosis,this paper uses the proposed method to experiment with the bearing data set provided by Case Western Reserve University(CWRU). The experiment proves that the proposed method has a better fault diagnosis performance compared with other traditional methods.

关键词： Fault diagnosis sparse autoencoder Batch-normalization

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Transformer Fault Diagnosis based on Deep Brief sparse autoencoder

Transformer Fault Diagnosis based on Deep Brief Sparse Autoe...

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第三十八届中国控制会议

作者： Zhong Xu Wenxiong Mo Yong Wang Simin Luo Tian Liu Electric Power Test & Research Institute Guangzhou Power Supply Bureau Co. Ltd

Dissolved gas analysis(DGA) is an effective way to diagnose the internal faults of transformer. This paper proposes a deep belief sparse autoencoder(DBSAE), which can be performed on DGA data to detect the transformer fault. The proposed method is constructed by two layers of sparse autoencoder and one layer of back propagation network. DBSAE adopts the unsupervised training to collect the features of DGA raw data, and the back propagation network with the supervised training could realize the transformer faults diagnosis. The experiments are performed on a real DGA dataset. The experimental results verify the effectiveness of the proposed method, which can extract features from the original data, and achieve a superior correct differentiation rate which reached 95.4% in average accuracy on transformer fault diagnosis.

关键词： Transformer fault diagnosis dissolved gas analysis sparse autoencoder deep belief network

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Pseudoinverse Learning Algorithom for Fast sparse autoencoder Training

Pseudoinverse Learning Algorithom for Fast Sparse Autoencode...

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IEEE Congress on Evolutionary Computation

作者： Bingxin Xu Ping Guo Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

sparse autoencoder is one approach to automatically learn features from unlabeled data and received significant attention during the development of deep neural networks. However, the learning algorithm of sparse autoencoder suffers from slow learning speed because of gradient descent based algorithms have many drawbacks. In this paper, a fast learning algorithm for sparse autoenceder is proposed which based on pseudoinverse learning algorithm (PIL). The proposed method calculates encoder weight matrix by truncating the pseudoinverse matrix of input data. The pseudoinverse truncation matrix is used as the weights of encoder, and then the input data is mapped to the hidden layer space through the biased ReLU activation function. The decoder weights are also can computed by the PIL. Unlike the gradient descent based algorithm, the proposed method does not require a time-consuming iterative optimization process and select many user-dependent parameters such as learning rate or momentum constant too. The experimental results indicate the superiority of proposed method which is very efficient and also can learned the sparsity of samples.

关键词： sparse autoencoder pseudoinverse learning algorithm fast learning data representation

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A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion

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EVOLUTIONARY INTELLIGENCE 2021年第1期14卷 133-149页

作者： Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam Maulana Abul Kalam Azad Univ Technol Kolkata 700068 India Univ Calcutta Kolkata 700098 India Netaji Subhash Engn Coll Kolkata 700152 India

This paper proposes a deep unsupervised learning based denoising autoencoder model for the restoration of degraded mammogram with visual interpretation of breast lumps or lesion in mammography images (called SSDAE). The proposed model attempts to intensify the underexposed and abnormal structural regions through noise elimination in mammography image. A deep stacked convolutional autoencoder is designed by combining the autoencoder and the deconvolution network which conjointly reduces noisy artifacts and improves image details in mammogram. The proposed SSDAE model takes large noisy mammogram image patches as input and extracts relevant features from target batches. The suggested model can extract relevant features and reduce the dimensionality through sparsity property of the image data while preserving the key features that have been applied to restore image data in feature space. In order to reconstruct a deafening mammogram, the proposed model is carried out through a patched base training on samples to suppress noise thereby preserving structural details in mammography imaging. Experimental results authenticate that the suggested SSDAE model outplays a number of state-of-the-art methods for both X-ray mammogram and ultrasonographic mammogram. The execution speed for target noisy images increases with fine tuning of the network when compared to other algorithms.

关键词： Deep learning Mammogram restoration Convolutional autoencoder sparse autoencoder KL divergence

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Face recognition based on deep aggregated sparse autoencoder network

Face recognition based on deep aggregated sparse autoencoder...

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第37届中国控制会议

作者： Guofeng Zou Dingyi Lin Gui-xia Fu Jin Shen Mingliang Gao College of Electrical and Electronic Engineering Shandong University of Technology

sparse autoencoder network is sensitive to face noise,and the learning process is easy to ignore the face structure *** this problem,we propose a face recognition approach fused sub-region LBP feature and deep aggregated sparse autoencoder ***,the face image is divided into different sub-regions,and the local binary pattern is used to pre-process the face image in order to obtain the LBP features of different sub-region ***,the deep features for different sub-region LBP features are extracted with different sparse ***,the output features of different sparse autoencoders are aggregated by full connection,and the face feature vectors are obtained for *** get the optimal aggregated network structure and network parameters through lots of experiments,while the face recognition accuracy is also improved.

关键词： Unsupervised deep learning sparse autoencoder deep aggregated network LBP feature sub-region division face recognition

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Improved sparse autoencoder based artificial neural network approach for prediction of heart disease

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Informatics in Medicine Unlocked 2020年 18卷

作者： Mienye, Ibomoiye Domor Sun, Yanxia Wang, Zenghui Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg 2006 South Africa Department of Electrical and Mining Engineering University of South Africa Florida 1709 South Africa

In this paper a two stage method is proposed to effectively predict heart disease. The first stage involves training an improved sparse autoencoder (SAE), an unsupervised neural network, to learn the best representation of the training data. The second stage involves using an artificial neural network (ANN) to predict the health status based on the learned records. The SAE was optimized so as to train an efficient model. The experimental result shows that the proposed method improves the performance of the ANN classifier, and is more robust as compared to other methods and similar scholarly works. © 2020 The Authors

关键词： ANN Deep learning Heart disease sparse autoencoder Unsupervised learning

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Classification of Alzheimer's Disease Using Stacked sparse Convolutional autoencoder 19

Classification of Alzheimer's Disease Using Stacked Sparse C...

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19th International Conference on Control, Automation and Systems (ICCAS)

作者： Baydargil, Husnu Baris Park, Jang-Sik Kang, Do-Young Kyungsung Univ Dept Elect & Commun Engn 309 Suyeong Ro Busan South Korea Dong A Univ Dong A Univ Hosp Dept Nucl Med Coll Med 37 Nakdong Daero550 Obaegosip BeHadan 2 I Dong Busan South Korea

ISBN: (纸本)9788993215182

Alzheimer's disease is a neurodegenerative disease that affects the brain structure and its functions. Early and accurate detection of AD through medical imaging may improve lifespan and overall quality of life for patients and their caretakers. In this paper, a specially developed sparse autoencoder is used to accurately detect AD in PET/CT (Positron Emission Tomography/ Computerized Tomography) brain images. Sagittal and coronal images were created from axial images, and those were trained separately to compare classification results. Two-stage training is utilized;first stage, a supervised training to train the classifier to identify the AD, and an unsupervised learning in order to produce an image output. In the created dataset, state-of-the-art classification models are trained and compared to the developed model. A 98.67% accuracy is reached for sagittal images. Detailed information is provided in chapters three and four.

关键词： Machine learning Deep learning Computer vision Medical imaging Alzheimer's disease Convolutional neural networks Image classification sparse autoencoder

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Towards precision medicine in Glioblastoma: Unraveling MGMT methylation status in glioblastoma using adaptive sparse autoencoders

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EGYPTIAN INFORMATICS JOURNAL 2025年 29卷

作者： Fazal, Sumaiya Rehman, Hafeez Ur Alazab, Moutaz Natl Univ Comp & Emerging Sci Dept Comp Sci Islamabad Pakistan Liverpool John Moores Univ Oryx Universal Coll Sch Comp & Data Sci Doha Qatar Al Balqa Appl Univ Fac Artificial Intelligence Dept Intelligent Syst Al Salt Jordan

Glioblastoma is a type of cancer known for its fast growth, invasive behavior, and resistance to different treatments. It accounts for more than 50% of all malignant brain tumors. Due to its complexity, it is crucial to customize treatment plans according to each patient's needs. The methylation status of O-6-methylguanineDNA methyltransferase (MGMT) is a crucial biomarker (Butler et al., 2020) for predicting patient response to treatment plans and plays a critical role in the treatment response and prognosis of patients with glioblastomas, which is an aggressive form of brain cancer. It is difficult to predict the MGMT methylation status using existing methods due to the heterogeneous contrast appearance, substantial variability within lesions, and irregular enhancement patterns associated with the MGMT signature in MR images. In this work, we propose a novel method ADAptive sparse autoencoders (ADAPT) to determine the methylation status of MGMT promoter gene from the signatures present in the MRI images. Our approach involves learning features by repeatedly producing artificial MRI images, followed by a tailored sparse autoencoder that incorporates an adaptive sparse penalty to predict the MGMT methylation status. To achieve this, we designed an autoencoder neural network to generate synthetic MRI slices which extract and learn features from numerous MR sequences. We then designed sparsity restrictions by modeling an adaptive sparse penalty along with employing transfer learning to predict the methylation status of the MRI slice. The behavior of adaptive sparse penalty adjusts itself and the learning parameters in accordance with changes in the data (i.e., contrast variations, tumoural location variation etc., in MR images). The proposed technique achieves significant improvements in MRI image synthesis, preserving the brain tissue, fat, and individual tumor structures in all MRI modality images. On top of that the adaptive sparse penalty parameter resulted in the

关键词： Genetic subtype MGMT methylation status Glioblastoma autoencoder sparse autoencoder Adaptive sparse penalty

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