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23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Lu, Run-kun Liu, Jian-wei Wang, Yuan-fang Xie, Hao-jie Zuo, Xin China Univ Petr Dept Automat Beijing 102249 Peoples R China

ISBN: (纸本)9783030161422;9783030161415

Multi-view learning is a learning problem that utilizes the various representations of an object to mine valuable knowledge and improve the performance of learning algorithm, and one of the significant directions of multi-view learning is sub-space learning. As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views. The algorithm has two stages, the first is to train auto-encoder of each view, and the second stage is to train a supervised network. Interestingly, the two stages share the weights partly and assist each other by co-training process. According to the experimental result, we can learn a well performed latent feature representation, and auto-encoder of each view has more powerful reconstruction ability than traditional auto-encoder.

关键词： Multi-view auto-encoder Co-training

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Towards reliable hepatocytic anatomy segmentation in laparoscopic cholecystectomy using U-Net with auto-encoder

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SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES 2023年第9期37卷 7358-7369页

作者： Alkhamaiseh, Koloud N. Grantner, Janos L. Shebrain, Saad Abdel-Qader, Ikhlas Western Michigan Univ Dept Elect & Comp Engn Kalamazoo MI 49008 USA Western Michigan Univ Homer Stryker MD Sch Med Kalamazoo MI USA

BackgroundMost bile duct (BDI) injuries during laparoscopic cholecystectomy (LC) occur due to visual misperception leading to the misinterpretation of anatomy. Deep learning (DL) models for surgical video analysis could, therefore, support visual tasks such as identifying critical view of safety (CVS). This study aims to develop a prediction model of CVS during LC. This aim is accomplished using a deep neural network integrated with a segmentation model that is capable of highlighting hepatocytic *** images from LC videos were annotated with four hepatocystic landmarks of anatomy segmentation. A deep autoencoder neural network with U-Net to investigate accurate medical image segmentation was trained and tested using fivefold cross-validation. Accuracy, Loss, Intersection over Union (IoU), Precision, Recall, and Hausdorff Distance were computed to evaluate the model performance versus the annotated ground *** total of 1550 images from 200 LC videos were annotated. Mean IoU for segmentation was 74.65%. The proposed approach performed well for automatic hepatocytic landmarks identification with 92% accuracy and 93.9% precision and can segment challenging ***, can potentially provide an intraoperative model for surgical video analysis and can be trained to guide surgeons toward reliable hepatocytic anatomy segmentation and produce selective video documentation of this safety step of LC.

关键词： Critical view of safety Laparoscopic cholecystectomy Deep learning Anatomy segmentation auto-encoder U-Net Challenging cases Surgical safety

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Graph-Collaborated auto-encoder Hashing for Multiview Binary Clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 10121-10133页

作者： Wang, Huibing Yao, Mingze Jiang, Guangqi Mi, Zetian Fu, Xianping Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Liaoning Peoples R China Changzhou Univ Sch Comp Sci & ArtificialIntelligence Changzhou 213164 Jiangsu Peoples R China

Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploit the valuable information from samples, which fails to take the local geometric structure of unlabeled samples into consideration. Moreover, hashing based on auto-encoders aims to minimize the reconstruction loss between the input data and binary codes, which ignores the potential consistency and complementarity of multiple sources data. To address the above issues, we propose a hashing algorithm based on auto-encoders for multiview binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called graph-collaborated auto-encoder (GCAE) hashing for multiview binary clustering. Specifically, we propose a multiview affinity graphs' learning model with low-rank constraint, which can mine the underlying geometric information from multiview data. Then, we design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively. Notably, we impose the decorrelation and code balance constraints on binary codes to reduce the quantization errors. Finally, we use an alternating iterative optimization scheme to obtain the multiview clustering results. Extensive experimental results on five public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-the-art alternatives.

关键词： Binary codes Clustering algorithms Feature extraction Semantics Data models Task analysis Quantization (signal) auto-encoder binary code graph-collaborated multiview clustering

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Conditional Variational auto-encoder and Extreme Value Theory Aided Two-Stage Learning Approach for Intelligent Fine-Grained Known/Unknown Intrusion Detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 3538-3553页

作者： Yang, Jian Chen, Xiang Chen, Shuangwu Jiang, Xiaofeng Tan, Xiaobin Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China

Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on organizations and information infrastructure. This paper aims at developing an intelligent intrusion detection system capable of classifying known attacks as well as inferring unknown ones. To achieve this, we formulate the problem of fine-grained known/unknown intrusion detection as a two-stage minimization problem, where the first stage is to seek a score measure for minimizing the empirical risk of misclassifying the known attacks, while the second stage is to find another score measure for minimizing the identification risk of inferring unknown attacks. The hierarchical nature of problem formulation allows us to employ the class conditioned auto-encoders to construct a hierarchical intrusion detection framework. Since the reconstruction errors of unknown attacks are generally higher than that of the known attacks, we further employ extreme value theory in the second stage to model the distribution of reconstruction errors for differentiating known/unknown attack. To further reduce the false positive rate, we add a benign clustering module for learning the multimodal distribution of benign traffic. We conduct an experiment on two widely used datasets for assessing intrusion detection. The results show that the proposed method improves the detection rate of unknown attacks while keeping a low false positive rate.

关键词： Intrusion detection Feature extraction Machine learning Support vector machines Anomaly detection Training Deep learning Intrusion detection artificial intelligence auto-encoder extreme value theory

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Efficient Non-Compression auto-encoder for Driving Noise-Based Road Surface Anomaly Detection

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2022年第11期17卷 1648-1656页

作者： Park, YeongHyeon Jung, JongHee SK Planet Co Ltd Seongnam South Korea

Wet weather makes water film over the road and that film causes lower friction between tire and road surface. When a vehicle passes the low-friction road, the accident can occur up to 35% higher frequency than a normal condition road. In order to prevent accidents as above, identifying the road condition in real-time is essential. Thus, we propose a convolutional auto-encoder-based anomaly detection model for taking both less computational resources and achieving higher anomaly detection performance. The proposed model adopts a non-compression method rather than a conventional bottleneck structured auto-encoder. As a result, the computational cost of the neural network is reduced up to 1 over 25 compared with the conventional models, and the anomaly detection performance is improved by up to 7.72%. Thus, we conclude the proposed model as a cutting-edge algorithm for real-time anomaly detection. (c) 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： anomaly detection auto-encoder non-compression road safety vehicle noise

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Deep Geo-Constrained auto-encoder for Non-Landmark GPS Estimation

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IEEE TRANSACTIONS ON BIG DATA 2019年第2期5卷 120-133页

作者： Jiang, Suhui Kong, Yu Fu, Yun Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Coll Engn Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Coll Comp & Informat Sci Boston MA 02115 USA

This paper addresses the problem of geotagging images, i.e., assigning GPS coordinates (i.e., latitude, longitude) to images using image contents. Due to the huge appearance variability of visual features across the world, the images' contents and their GPS coordinates may be inconsistent. This means images captured from geographically close areas may appear visually distinct;and images with visually similar contents may be taken from geographically distant areas. In this paper, we propose a deep Geo-constrained auto-encoder (DGAE) to solve these inconsistency problems. Using clustered GPS data and visual data, our approach identifies inconsistent data pairs (i.e., image, GPS). We then propose a novel deep learning framework that can learn similar feature representations for geographically close images and distinct feature representations for geographically distant images. We introduce two new constraints: the same-area constraint and the easy-confusing constraint to our feature learning networks. The former one penalizes images from the same area but with very distinct visual features, and the latter one penalizes images from distant areas but with very similar visual features. A deep architecture is developed to further improve learning discriminative features, which can disambiguate different geometric locations. Our approach is extensively evaluated on a newly-compiled large image geotagging dataset from large-scale community-contributed images with 664,720 images and outperforms comparison approaches.

关键词： GPS estimation deep learning auto-encoder

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Attention-based misaligned spatiotemporal auto-encoder for video anomaly detection

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 285-297页

作者： Yang, Haiyan Liu, Shuning Wu, Mingxuan Chen, Hongbin Zeng, Delu Guilin Univ Elect Technol Sch Informat & Commun Jinji Rd Guilin 541004 Peoples R China South China Univ Technol Sch Elect & Informat Engn Wushan Rd Guangzhou 510641 Peoples R China

To face the shortcomings of the auto-encoder (AE) algorithm in terms of weak dynamic object extraction and strong reconstruction ability in abnormal situations for video anomaly detection, an attention-based misaligned spatiotemporal AE (AMS-AE) model is proposed for video anomaly detection. The AMS-AE model uses a pseudo-abnormal generation module (PAGM) to dislocate the spatiotemporal information of the video and then uses an attention mechanism algorithm to selectively capture the foreground regions. Finally, it reconstructs the video frames through encoding and decoding. During the training process, normal data is used for training, while the abnormal frames generated by the PAGM are introduced as anomalies to limit the reconstruction ability of the auto-encoding algorithm on abnormal data, which facilitates better detection of anomalies during testing phase. The experimental results show that the AMS-AE algorithm achieves significantly better performance than some other algorithms on three public datasets (UCSD Ped2, CUHK Avenue, and Shanghai Tech), effectively improving the accuracy and robustness of video anomaly detection. The area under the ROC curve (AUC) values of 98.7%, 89.7%, and 74.0% were achieved on the above public datasets, respectively.

关键词： Video anomaly detection Pseudo-anomaly generation module Attention mechanism auto-encoder

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Event log anomaly detection method based on auto-encoder and control flow

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MULTIMEDIA SYSTEMS 2024年第1期30卷 1-15页

作者： Kan, Daoyu Fang, Xianwen Anhui Univ Sci & Technol Coll Math & Big Data 168 Taifeng St Huainan 232001 Anhui Peoples R China Anhui Prov Engn Lab Big Data Anal & Early Warning Huainan Peoples R China

Anomaly detection is widely used in the field of business process management, and researchers have proposed various anomaly detection algorithms to detect anomalies in event logs. However, existing research focuses on detecting anomalies in event logs at the data level, ignoring the problem of anomalies caused by event log control flow, especially behavioral relationships, and identifying behavioral anomalies as normal, leading to an increase in the false-negative rate of anomaly detection results, which negatively affects the performance of process mining. To solve the above problems, this article proposes an auto-encoder-based anomaly detection approach to achieve the detection of behavioral relationship anomalies in event logs through the reconstruction error between images. The approach first considers event logs containing behavioral relationships, converts the logs into images as input to the auto-encoder, and analyses the reconstruction error between images to propose a reconstruction error threshold for anomaly detection. The algorithm is able to achieve anomaly detection of behavioral relationships in event logs and reduce the false-negative rate of anomaly detection results. Experiments on synthetic datasets and real datasets show that the proposed approach can improve the recall rate and F1-score of event log anomaly detection effectively.

关键词： Control flow Anomaly detection auto-encoder Business process Reconstruction error

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Fuzzy One-Class Extreme auto-encoder

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NEURAL PROCESSING LETTERS 2019年第1期50卷 701-727页

作者： Yu, Hualong Sun, Dan Xi, Xiaoyan Yang, Xibei Zheng, Shang Wang, Qi Jiangsu Univ Sci & Technol Sch Comp 2 Mengxi Rd Zhenjiang 212003 Jiangsu Peoples R China

A novel one class classification (OCC) algorithm called fuzzy one class extreme auto-encoder (FOCEAE) is presented in this article. The algorithm combines the precision of probability density estimation and the generalization of neural networks to accurately generate the compact bound for the target class cases. Firstly, a K-nearest-neighbors non-parametric probability density estimation-alike strategy is used to estimate the relative densities of all target class training objects, then the relative densities are transformed to be the fuzzy coefficients for further training fuzzy extreme learning machine (FELM) model. Specifically, considering there are only one-class instances, FELM is trained in the form of auto-encoder, i.e., each input equals to be the expected output of the network. Finally, the bound (i.e., the threshold) of the target class cases is determined by calculating and ranking the reconstructed errors of all training instances. We show the effectiveness and superiority of the proposed FOCEAE algorithm by comparing it with some benchmark OCC algorithms on a mass of data sets in terms of both F-measure and G-mean metrics. The statistical results also indicate that the proposed algorithm performs significantly better than some conventional ones.

关键词： One class classification Fuzzy extreme learning machine auto-encoder Probability density estimation K nearest neighbors Reconstruction error

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Dual auto-encoder Based Rating Prediction Recommendation Algorithm

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IEEE ACCESS 2022年 10卷 97289-97297页

作者： Xin, Gaowei Qin, Jiwei Song, Xiaoyuan Zheng, Jiong Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Signal Detect & Proc Urumqi 830046 Xinjiang Uygur Peoples R China

Collaborative filtering is the most widely used method in recommendation algorithms, but it still faces the serious problem of data sparsity. Traditional collaborative filtering uses matrix decomposition to learn the latent features of users and items. As an extension model of matrix decomposition, Funk-SVD model has attracted wide attention due to its good scalability and easy implementation, but it is difficult to extract the latent features of users and items from sparse rating information because it essentially learns the linear relationship between users and items. To solve this problem, we propose a Dual auto-encoder based Rating Prediction Recommendation Algorithm (DRPRA) model. The DRPRA model uses the strong ability of deep learning in feature learning, which combines double auto-encoders with Funk-SVD. First, the auto-encoder captures the latent features of users and items respectively. Then, the Funk-SVD combines the user features with item features to reconstruct the rating matrix. After that, we minimize the error between original rating matrix and reconstructed rating matrix, and to alleviate the problem of data sparsity and improve the accuracy of rating prediction effectively. We conducted extensive experiments on Movielens-100K, Movie Tweeting-10k, and Film Trust datasets, and the results show that the rating prediction model based on dual auto-encoders has a superior recommendation performance.

关键词： Feature extraction Predictive models Prediction algorithms Representation learning Matrix decomposition Deep learning Data models Recommendation algorithm auto-encoder Funk-SVD sparsity

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