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Hyperspectral image classification using an extended auto-encoder method

SIGNAL PROCESSING-IMAGE COMMUNICATION 2021年 92卷 116111-116111页

作者： Ghasrodashti, Elham Kordi Sharma, Nabin Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Technol Sydney Sch Comp Sci Fac Engn & Informat Technol Sydney NSW Australia

This article proposes a spectral-spatial method for classification of hyperspectral images (HSIs) by modifying traditional auto-encoder based on Majorization Minimization (MM) technique. The proposed method consists of suggesting three main modifications. First, to construct weights of auto-encoder, similarity angle map(SAM) criterion is used as regularization term. It is useful to extract spectral similarity of initial features. Second, to enhance the classification accuracy, fuzzy mode is used to estimate parameters. These modifications lead to create an extended auto-encoder based on MM (EAEMM). Third, to improve the performance of auto encoder, multi-scale features (MSF) are extracted. In comparison with some of the state-of-the-art methods, the experimental results obtained using the proposed method (MSF-EAEMM) show that it significantly improves the classification accuracy of HSI classification.

关键词： Hyperspectral image Image classification auto-encoder

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automatic classification of nerve discharge rhythms based on sparse auto-encoder and time series feature

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BMC BIOINFORMATICS 2022年第SUPPL 3期22卷 619-619页

作者： Jiang, Zhongting Wang, Dong Chen, Yuehui Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China

Background Nerve discharge is the carrier of information transmission, which can reveal the basic rules of various nerve activities. Recognition of the nerve discharge rhythm is the key to correctly understand the dynamic behavior of the nervous system. The previous methods for the nerve discharge recognition almost depended on the traditional statistical features, and the nonlinear dynamical features of the discharge activity. The artificial extraction and the empirical judgment of the features were required for the recognition. Thus, these methods suffered from subjective factors and were not conducive to the identification of a large number of discharge rhythms. Results The ability of automatic feature extraction along with the development of the neural network has been greatly improved. In this paper, an effective discharge rhythm classification model based on sparse auto-encoder was proposed. The sparse auto-encoder was used to construct the feature learning network. The simulated discharge data from the Chay model and its variants were taken as the input of the network, and the fused features, including the network learning features, covariance and approximate entropy of nerve discharge, were classified by Softmax. The results showed that the accuracy of the classification on the testing data was 87.5%, which could provide more accurate classification results. Compared with other methods for the identification of nerve discharge types, this method could extract the characteristics of nerve discharge rhythm automatically without artificial design, and show a higher accuracy. Conclusions The sparse auto-encoder, even neural network has not been used to classify the basic nerve discharge from neither biological experiment data nor model simulation data. The automatic classification method of nerve discharge rhythm based on the sparse auto-encoder in this paper reduced the subjectivity and misjudgment of the artificial feature extraction, saved the time for the co

关键词： Nerve discharge Classification Neural network auto-encoder Softmax Feature learning

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Unsupervised feature selection via transformed auto-encoder

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KNOWLEDGE-BASED SYSTEMS 2021年 215卷 106748-106748页

作者： Zhang, Yunhe Lu, Zhoumin Wang, Shiping Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350116 Peoples R China

As one of the fundamental research issues, feature selection plays a critical role in machine learning. By the removal of irrelevant features, it attempts to reduce computational complexities of upstream tasks, usually with computation accelerations and performance improvements. This paper proposes an auto-encoder based scheme for unsupervised feature selection. Due to the inherent consistency, this framework can solve traditional constrained feature selection problems approximately. Specifically, the proposed model takes non-negativity, orthogonality, and sparsity into account, whose internal characteristics are exploited sufficiently. It can also employ other loss functions and flexible activation functions. The former can fit a wide range of learning tasks, and the latter has the ability to play the role of regularization terms to impose regularization constraints on the model. Thereinafter, the proposed model is validated on multiple benchmark datasets, where various activation and loss functions are analyzed for finding better feature selectors. Finally, extensive experiments demonstrate the superiority of the proposed method against other compared state-of-the-arts. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Machine learning Deep learning Feature selection Unsupervised learning auto-encoder

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A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 150卷 107233-107233页

作者： Mao, Wentao Feng, Wushi Liu, Yamin Zhang, Di Liang, Xihui Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China Univ Manitoba Dept Mech Engn Winnipeg MB R3T 5V6 Canada

In recent years, deep learning techniques have been proved a promising tool for bearing fault diagnosis. However, to extract deep features with better representative ability, how to introduce discriminant information about different fault types into the deep learning model is still challenging. Moreover, as deep learning techniques heavily rely on mass of measuring data, relatively small amounts of data may cause over-fitting and reduce model stability as well. To solve such problems, a new deep auto-encoder method with fusing discriminant information about multiple fault types is proposed for bearing fault diagnosis. First, a new loss function is designed by introducing structural discriminant information. Specifically, to improve the feature's representative ability, a new discriminant regularizer is designed in the loss function by using maximum correlation entropy. And to represent the structural information among multiple fault types, a relation matrix for fault types is introduced, then a new regularizer with a symmetric constraint on this matrix is constructed. Second, a gradient descent method is provided to optimise this loss function, and the optimal deep features, as well as fault relatedness, are learned simultaneously. Experimental results on CWRU and IMS bearing data sets show that, compared to several state-of-the-art diagnosis methods, the proposed method can effectively improve the diagnostic accuracy with acceptable time efficiency. And the results on the Kruskal-Wallis Test indicate the proposed method has better numerical stability. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Bearing fault diagnosis auto-encoder Deep learning Structural information Discriminant information

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TRAFFIC DATA ANALYSIS USING DEEP ELMAN AND GATED RECURRENT auto-encoder

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NEURAL NETWORK WORLD 2021年第6期30卷 347-363页

作者： Mehralian, S. Teshnehlab, M. Nasersharif, B. KN Toosi Univ Technol Comp Engn Fac Tehran Iran KN Toosi Univ Technol Ind Control Ctr Excellence Tehran Iran

Traffic flow prediction is one of the most interesting machine learning applications in real-world problems that can help anyone move around. In this study, we proposed a feature extraction structure for multivariate time series using Elman recurrent auto-encoder. We added loopback from the encoder layer of the normal auto-encoder to regard sequence information between successive data. The feedback layer implemented using Elman neural network and GRU cells, then the model is trained by different optimization algorithms. The models are also trained using the Emotional Learning method in which we involve the derivative of the error in the cost function to avoid local minimums and keep the last state of the network. We used the proposed method for classification and prediction problems on traffic data from the California Department of Transportation Performance Measurement System (PeMS). The results show that our structure can successfully extract a compact representation of traffic data useful for reconstructing of original data, classification, and prediction. The results also show that adding the recurrent layer to the feature extractor (auto-encoder) leads to better results in the classification phase in comparison with standard methods that do not use the recurrence during feature extraction.

关键词： deep learning recurrent neural network auto-encoder traffic data analysis

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Cramer-Wold auto-encoder

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 6594-6621页

作者： Szymon Knop Przemysław Spurek Jacek Tabor Igor Podolak Marcin Mazur Stanisław Jastrzebski Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland Center of Data Science Department of Radiology New York University New York

The computation of the distance to the true distribution is a key component of most state-of-the-art generative models. Inspired by prior works on the Sliced-Wasserstein auto-encoders (SWAE) and the Wasserstein auto-encoders with MMD-based penalty (WAE-MMD), we propose a new generative model - a Cramer-Wold auto-encoder (CWAE). A fundamental component of CWAE is the characteristic kernel, the construction of which is one of the goals of this paper, from here on referred to as the Cramer-Wold kernel. Its main distinguishing feature is that it has a closed-form of the kernel product of radial Gaussians. Consequently, CWAE model has a closed-form for the distance between the posterior and the normal prior, which simplifies the optimization procedure by removing the need to sample in order to compute the loss function. At the same time, CWAE performance often improves upon WAE-MMD and SWAE on standard benchmarks.

关键词： auto-encoder generative model Wasserstein auto-encoder Cramer-Wold theorem deep neural network

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Near-infrared fault detection based on stacked regularized auto-encoder network

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年 204卷 104101-104101页

作者： Chen Zihao Luan Xiao-li Liu Fei Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

Near-infrared spectroscopy as a fast, efficient, non-destructive, and non-polluting technology is employed in this paper to acquire process data at the molecular level to detect the process fault. Due to the high intrinsic dimension, information redundancy, multiple linear correlations between variables and high-sensitiveness of spectral data, stacked auto-encoder as an unsupervised learning method is utilized. In addition, for avoiding over fitting of these auto-encoders and making the model more general, L2 regularization term is employed. To realize the detection and classification of multi-class faults, the softmax regression is adopted as a multi-classification algorithm. Finally, the eligibility of the mentioned neural network is investigated through a crude oil desalination and dehydration process. Nonlinear dimensional reduction of spectral data with minimizing the information loss during data compression, high accuracy, and mull-class fault detection with good visualization of its decision edge are three main advantages of the proposed fault detection method comparing with existing methods.

关键词： Near infrared spectroscopy auto-encoder Regularization Softmax regression Fault detection

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Weakly-supervised auto-encoder via energy regularization and soft multi-label learning on k labeled samples

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NEUROCOMPUTING 2024年 585卷

作者： Wang, Huiling Sun, Jun Gu, Xiaofeng Hu, Zunhao Zhou, Chao Jiangnan Univ Sch IoT Engn Wuxi 214122 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China

Image classification is a hot topic in computer vision tasks. As a simple unsupervised network model, autoencoder can learn and apply features to classification. However, due to the lack of prior knowledge of autoencoder, there are significant limitations in their performance in practical applications. On the contrary, although precise labels can guide supervised learning to learn accurately, it is difficult to obtain strong supervised information on many tasks because of the high cost of data annotation. In this paper, we propose a weaklysupervised auto-encoder via energy regularization and soft multi-label learning on k labeled samples (WSAE), where weakly-supervised learning not only reduces the workload of manual labeling but also introduces supervised information, which improves the performance of auto-encoder to a large extent. Specifically, we first explore a lower bound value of training data that needs to be labeled in weakly supervised learning. Then, we discriminate the visual consistency of k labeled samples compared to unlabeled samples with soft multi-label to efficiently increase the difference of features between classes and reduce the variation of features within classes for more discriminative feature representations and higher classification accuracy. In addition, an energy regularization is introduced to the model to fit the probability distribution of the data so that the smaller the energy, the more concentrated the probability distribution and the more ordered the system. The model with energy regularization can represent the data better, thereby improving the feature extraction ability and increasing the classification accuracy of the network. Extensive experiments are conducted on some publicly available datasets and demonstrate that our method outperforms the state-of-the-art methods.

关键词： auto-encoder Unsupervised learning Supervised learning Weakly-supervised learning Soft multi-label Energy regularization

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Design of Ensemble Stacked auto-encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

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MICROMACHINES 2018年第8期9卷 411-411页

作者： Lee, Jae-Neung Byeon, Yeong-Hyeon Kwak, Keun-Chang Chosun Univ Dept Control & Instrumentat Engn 375 Seosuk Dong Gwangju 501759 South Korea

This paper discusses the classification of horse gaits for self-coaching using an ensemble stacked auto-encoder (ESAE) based on wavelet packets from the motion data of the horse rider. For this purpose, we built an ESAE and used probability values at the end of the softmax classifier. First, we initialized variables such as hidden nodes, weight, and max epoch using the options of the auto-encoder (AE). Second, the ESAE model is trained by feedforward, back propagation, and gradient calculation. Next, the parameters are updated by a gradient descent mechanism as new parameters. Finally, once the error value is satisfied, the algorithm terminates. The experiments were performed to classify horse gaits for self-coaching. We constructed the motion data of a horse rider. For the experiment, an expert horse rider of the national team wore a suit containing 16 inertial sensors based on a wireless network. To improve and quantify the performance of the classification, we used three methods (wavelet packet, statistical value, and ensemble model), as well as cross entropy with mean squared error. The experimental results revealed that the proposed method showed good performance when compared with conventional algorithms such as the support vector machine (SVM).

关键词： motion analysis auto-encoder dance classification deep learning self-coaching wavelet packet classification of horse gaits

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Breast Histopathological Image Classification Based on auto-encoder Reconstructed Domain Adaptation

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APPLIED SCIENCES-BASEL 2024年第24期14卷 11802页

作者： Wang, Pin Zhang, Jinhua Li, Yongming Guo, Yurou Li, Pufei Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China

As an effective computer-aided diagnostic tool, deep learning has been successfully applied to the classification of breast histopathological images. However, the performance of the deep model is data-driven, and it is difficult to obtain satisfied results when the number of histopathological images is small and labelling histopathological images is difficult. Moreover, in traditional deep learning methods, the representation of features is monotonous, which leads to the limitation of the classification performance of the model. This study proposes an auto-encoder reconstructed semi-supervised domain adaptation for a breast histopathological image classification algorithm. First, the model was pre-trained and transferred to extract high-level features of the sample images. Then, the encoding and decoding parts of the auto-encoder were used to reconstruct the feature representation learning and the sample feature reconstruction learning, respectively. This ensured that the useful information for the classification was purified and retained. At the same time, the domain discriminator was used to confuse the source and target domain features to enhance the learning ability of the model. Finally, the distribution difference of features at different depths of the auto-encoder was measured to minimize the discrepancy of feature distribution between domains, so as to complete the classification of histopathological images. Compared to the results of the comparative and ablation algorithms from the BreakHis to SNL datasets, the proposed method achieved the best results in terms of F1 score (93.40%), accuracy (95.24%), sensitivity (94.66%), and specificity (95.56%). The experimental results demonstrate that the proposed method achieves a remarkable classification performance.

关键词： breast histopathological image classification auto-encoder semi-supervised domain adaptation

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