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12th International Conference on Intelligent Systems and Knowledge Engineering (IEEE ISKE)

作者： Du, Yimin Wu, Guixing Tang, Guolin Univ Sci & Technol China Sch Software Engn Hefei Anhui Peoples R China Beijing Univ Technol Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781538618295

Clustering plays an important role in data mining and machine learning. Then, intuitionistic fuzzy sets (IFSs) are flexible and practical in dealing with vagueness and uncertainty problems. To cluster the information expressed by intuitionistic fuzzy data, this paper proposes the joint training auto-encoder based intuitionistic fuzzy clustering algorithm. Firstly, we propose the auto-encoder based intuitionistic fuzzy clustering by utilizing similarity measure of IFSs, auto encoder and k-means algorithm. Then, we propose the joint training auto-encoder based intuitionistic fuzzy clustering algorithm by utilizing the proposed auto encoder based intuitionistic fuzzy clustering and two kinds of similarity measures for the clustering analysis of intuitionistic fuzzy data. Lastly, several experiments are provided to verify the effectiveness of the proposed intuitionistic fuzzy clustering algorithms.

关键词： intuitionistic fuzzy set similarity measure auto-encoder clustering

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auto-encoder and LSTM-Based Credit Card Fraud Detection

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SN Computer Science 2023年第5期4卷 1-8页

作者： Sehrawat, Deepthi Singh, Yudhvir UIET MDU Rohtak India Computer Science and Engineering UIET MDU Rohtak India

The increased fraud risk due to the most recent methods of paying with a credit card, such as real-time payments and cards with near-field communication (NFC) capabilities, makes detecting credit card fraud an essential topic of study. Deep learning has shown encouraging results in recent years, whenever it is used to detect credit card fraud. There have been several deep learning-based models presented in this field of research. Still, not all of them have proven to be the most effective because each technique is best used with a particular dataset, and fraudsters constantly refine their methods to evade detection by the systems in place today. This work's primary focus is detecting credit card fraud using an auto-encoder with GRU and LSTM models. This is an efficient mechanism;first, data are passed to auto-encoder without the labels, and after that, the output generated by the auto-encoder is passed to the LSTM model as input with labels to detect fraud. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： auto-encoder Credit card Gated recurrent unit (GRU) Long short-term memory (LSTM) Performance metrics

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auto-encoder Based Model for High-Dimensional Imbalanced Industrial Data 28th

Auto-Encoder Based Model for High-Dimensional Imbalanced Ind...

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28th International Conference on Neural Information Processing

作者： Zhang, Chao Bom, Sthitie Seagate Technol Bloomington MN 55435 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9783030923068;9783030923075

With the proliferation of IoT devices, the distributed control systems are now capturing and processing more sensors at higher frequency than ever before. These new data, due to their volume and novelty, cannot be effectively consumed without the help of data-driven techniques. Deep learning is emerging as a promising technique to analyze these data, particularly in soft sensor modeling. The strong representational capabilities on complex data and the flexibility it offers from an architectural perspective make it an active topic of applied research in industrial settings. However, the successful applications of deep learning in soft sensing are still not widely integrated in factory control systems, because most of the research on soft sensing do not have access to large scale industrial data which are varied, noisy and incomplete. The results published in most research papers are therefore not easily reproduced when applied to the variety of data in industrial settings. Here we provide manufacturing data sets that are much larger and more complex than public open soft sensor data. Moreover, the data sets are from Seagate factories on active service with only necessary anonymization, so that they reflect the complex and noisy nature of real-world data. We introduce a variance weighted multi-headed quality-driven auto-encoder classification model that fits well into the high-dimensional and highly imbalanced data. Besides the use of weighting or sampling methods to handle the highly imbalanced data, the model also simultaneously predicts multiple outputs by exploiting output-supervised representation learning and multi-task weighting.

关键词： auto-encoder Big data Deep learning Process control Multi-task learning Soft sensing Wafer production

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A semi-supervised auto-encoder using label and sparse regularizations for classification

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APPLIED SOFT COMPUTING 2019年 77卷 205-217页

作者： Chai, Zhilei Song, Wei Wang, Huiling Liu, Fei Jiangnan Univ Sch IoT Engn Wuxi Peoples R China Minist Educ Engn Res Ctr Internet Things Appl Technol Beijing Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi Peoples R China Wuxi Taihu Univ Sch IoT Engn Wuxi Peoples R China

The semi-supervised auto-encoder (SSAE) is a promising deep-learning method that integrates the advantages of unsupervised and supervised learning processes. The former learning process is designed to extract the underlying concepts of data as intrinsic information and enhance its generalization ability to express data. Furthermore, the supervised process tends to describe the rules of categorization with labels that further improve categorization accuracy. In this paper, we propose a novel semi-supervised learning method, namely, label and sparse regularization AE (LSRAE), by integrating label and sparse constraints to update the structure of the AE. The sparse regularization activates a minority of important neurons, while most of the other neurons are inhibited. Such a method ensures that LSRAE can yield a more local and informative structure of the data. Moreover, by implementing the label constraint, the supervised learning process can extract the features regulated by category rules and enhance the performance of the classifier in depth. To extensively test the performances of LSRAE, we perform our experiments on the benchmark datasets USPS, ISOLET and MNIST. The experimental results demonstrate the superiority of LSRAE in comparison with state-of-the-art feature extraction methods including AE, LSAE, SAE, ELM, DBN, and adaptive DBN. (C) 2019 Elsevier B.V. All rights reserved.

关键词： auto-encoder Semi-supervised learning Classification ELM DBN

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Estimation of gait normality index based on point clouds through deep auto-encoder

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2019年第1期2019卷 1-13页

作者： Trong-Nguyen Nguyen Meunier, Jean Univ Montreal DIRO Pavillon Andre Aisenstadt2920 Chemin Tour Montreal PQ H3T 1J4 Canada

This paper proposes a method estimating an index that indicates human gait normality based on a sequence of 3D point clouds representing the walking motion of a subject. A cylinder-based histogram is extracted from each cloud to reduce the number of data dimensions as well as highlight gait-related characteristics. A model of deep neural network is finally formed from such histograms of normal gait patterns to provide gait normality indices supporting gait assessment tasks. The ability of our approach is demonstrated using a dataset of 9 different gait types performed by 9 subjects and two other datasets converted from mocap data. The experimental results are also compared with other related methods that process different input data types including silhouette, depth map, and skeleton as well as state-of-the-art deep learning approaches working on point cloud.

关键词： Gait Normality index auto-encoder Deep network

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Robust image Translation and Completion Based on Dual auto-encoder With Bidirectional Latent Space Regression

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IEEE ACCESS 2019年 7卷 58695-58703页

作者： Lee, Sukhan Ul Islam, Naeem Sungkyunkwan Univ Intelligent Syst Res Inst Dept Elect & Comp Engn Suwon 440746 South Korea

automated image translation and completion is a subject of keen interest due to their impact on image representation, interpretation, and enhancement. To date, a conditional or a dual adversarial framework with a convolutional auto-encoder embedded as a generator is known to offer the best accuracy in image translation. However, although the frequency is excellent, the adversarial framework may suffer from a lack of generality, i.e., the accuracy drops when translating incomplete and corrupted data given as untrained noisy input data. This paper proposes an approach to robust image-to-image translation that offers a high level of generality while keeping accuracy high as well. The proposed approach is referred to here as a dual auto-encoder with bidirectional latent space regression or Bi-directionally Associative DualAE, for short. The proposed BA-DualAE is configured with two auto-encoders the individual latent spaces of which are tightly associated by a bidirectional regression network. Once the two auto-encoders are trained independently for their respective domains, and then, the bidirectional regression network is trained to learn the general association between data pairs. With its capability of robust and bidirectional image translation, BA-DualAE performed direct image completion with no iterative search. The experiments with photo-sketch datasets demonstrated that the proposed BA-DualAE is highly robust under incomplete or corrupted data conditions and is far superior to adversarial frameworks in terms of generality and robustness.

关键词： Convolutional neural network auto-encoder bidirectional latent space regression image-to-image translation generative adversarial network

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Deep multi-kernel auto-encoder network for clustering brain functional connectivity data

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NEURAL NETWORKS 2021年 135卷 148-157页

作者： Lu, Hu Liu, Saixiong Wei, Hui Chen, Chao Geng, Xia Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Fudan Univ Sch Comp Sci Lab Cognit & Model Algorithm Shanghai 200433 Peoples R China

In this study, we propose a deep-learning network model called the deep multi-kernel auto-encoder clustering network (DMACN) for clustering functional connectivity data for brain diseases. This model is an end-to-end clustering algorithm that can learn potentially advanced features and cluster disease categories. Unlike other auto-encoders, DMACN has an added self-expression layer and standard back propagation is used to learn the features that are beneficial for clustering brain functional connectivity data. In the self-expression layer, the kernel matrix is constructed to extract effective features and a new loss function is proposed to constrain the clustering portion, which enables the training of a deep neural learning network that tends to cluster. To test the performance of the proposed algorithm, we applied the end-to-end deep unsupervised clustering algorithm to brain connectivity data. We then conducted experiments based on four public brain functional connectivity data sets and our own functional connectivity data set. The DMACN algorithm yielded good results in various evaluations compared with the existing clustering algorithm for brain functional connectivity data, the deep auto-encoder clustering algorithm, and several other relevant clustering algorithms. The deep learning-based clustering algorithm has great potential for use in the unsupervised recognition of brain diseases. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Deep neural network Brain functional connectivity auto-encoder Multi-kernel Disease diagnosis

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A Specific and Selective Neural Response Representation With Decorrelating auto-encoder

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IEEE ACCESS 2019年 7卷 70011-70020页

作者： Zhou, Jie Chen, Qian Jiang, Hao Cai, Shiqing Shao, Genfu Kikuchi, Hisakazu Nanjing Univ Informat Sci & Technol Coll Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China Hangzhou Dianzi Univ Coll Commun Engn Hangzhou 310000 Zhejiang Peoples R China Niigata Univ Coll Elect & Elect Engn Niigata 9502181 Japan

Since the pioneering report with an unsupervised pre-training principle was published, deep architectures, as a simulation of primary cortexes, have been intensively studied and successfully utilized in solving some recognition tasks. Motivated by that, herein, we propose a decorrelating regularity on autoencoders, named decorrelating auto-encoder (DcA), which can be stacked to deep architectures, called the SDcA model. The learning algorithm is designed based on the principles of redundancy-reduction and the infomax, and a fine-tuning algorithm based on correlation detecting criteria. The property of our model is evaluated by auditory and handwriting recognition tasks with the TIMIT acoustic-phonetic continuous speech corpus and MNIST database. The results show that our model has a general advantage as compared with four existing models, especially in low levels, and when training samples are scarce our model put up stronger learning capacity and generalization.

关键词： Machine learning pattern recognition deep architecture auto-encoder de-correlation

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QoS Prediction for Mobile Edge Service Recommendation With auto-encoder

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IEEE ACCESS 2019年 7卷 62312-62324页

作者： Yin, Yuyu Zhang, Weipeng Xu, Yueshen Zhang, He Mai, Zhida Yu, Lifeng Hangzhou Dianzi Univ Sch Comp Hangzhou 310018 Zhejiang Peoples R China Minist Educ Key Lab Complex Syst Modeling & Simulat Hangzhou 310018 Zhejiang Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Shaanxi Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Xanten Guangdong Dev Co Ltd Foshan 528200 Peoples R China Hithink RoyalFlush Informat Network Co Ltd Hangzhou 310023 Zhejiang Peoples R China

In the mobile edge computing environment, there are a large number of mobile edge services which are the carriers of various mobile intelligent applications. So how to recommend the most suitable candidate from such a huge number of available services is an urgent task, especially the recommendation task based on quality-of-service (QoS). In traditional service recommendation, collaborative filtering (CF) has been studied in academia and industry. However, due to the mobility of users and services, there exist several defects that limit the application of the CF-based methods, especially in an edge computing environment. The most important problem is the cold-start. In this paper, we propose an ensemble model which combines the model-based CF and neighborhood-based CF. Our approach has two phases, i.e., global features learning and local features learning. In the first phase, to alleviate the cold-start problem, we propose an improved auto-encoder which deals with sparse inputs by pre-computing an estimate of the missing QoS values and can obtain the effective hidden features by capturing the complex structure of the QoS records. In the second phase, to further improve prediction accuracy, a novel computation method is proposed based on Euclidean distance that aims to address the overestimation problem. We introduce two new concepts, common invocation factor and invocation frequency factor, in similarity computation. Then we propose three prediction models, containing two individual models and one hybrid model. The two individual models are proposed to utilize user similar neighbors and service similar neighbors, and the hybrid model is to utilize all neighbors. The experiments conducted in a real-world dataset show that our models can produce superior prediction results and are not sensitive to parameter settings.

关键词： QoS prediction service recommendation auto-encoder features learning similarity computation

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End-to-End Self-Driving Approach Independent of Irrelevant Roadside Objects With auto-encoder

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第1期23卷 641-650页

作者： Wang, Tinghan Luo, Yugong Liu, Jinxin Chen, Rui Li, Keqiang Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

On a highway, the frequency of occurrence of irrelevant features, such as trees, varies a lot in different scenes. A limitation of the deep conventional neural networks used in end-to-end self-driving systems is that if the incoming images contain too much information, it makes it difficult for the network to extract only the subset of features required for decision making. Consequently, while existing end-to-end approaches may perform well in training scenes, they may not work correctly in other scenes. In this study, we developed a novel training method for an auto-encoder that equips it to ignore irrelevant features in input images while simultaneously retaining relevant features. Compared with feature extraction methods in existing end-to-end approaches, the proposed method reduces the labeling costs by only requiring image-level tags. The method was validated by training a convolutional neural network model to process the output of the encoder and produce a steering angle to control the vehicle. The entire end-to-end self-driving approach can ignore the influence of irrelevant features even though there are no such features when training the convolutional neural network.

关键词： Feature extraction Training Task analysis Roads Decision making autonomous vehicles Neural networks End-to-end self-driving auto-encoder irrelevant features

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