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ScInfoVAE: interpretable dimensional reduction of single cell transcription data with variational autoencoders and extended mutual information regularization

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BIODATA MINING 2023年第1期16卷 17-17页

作者： Pan, Weiquan Long, Faning Pan, Jian Yulin Normal Univ Sch Math & Stat Yulin Peoples R China Yulin Normal Univ Sch Comp Sci & Engn Yulin Peoples R China

Single-cell RNA-sequencing (scRNA-seq) data can serve as a good indicator of cell-to-cell heterogeneity and can aid in the study of cell growth by identifying cell types. Recently, advances in variational autoencoder (VAE) have demonstrated their ability to learn robust feature representations for scRNA-seq. However, it has been observed that VAEs tend to ignore the latent variables when combined with a decoding distribution that is too flexible. In this paper, we introduce ScInfoVAE, a dimensional reduction method based on the mutual information variational autoencoder (InfoVAE), which can more effectively identify various cell types in scRNA-seq data of complex tissues. A joint InfoVAE deep model and zero-inflated negative binomial distributed model design based on ScInfoVAE reconstructs the objective function to noise scRNA-seq data and learn an efficient low-dimensional representation of it. We use ScInfoVAE to analyze the clustering performance of 15 real scRNA-seq datasets and demonstrate that our method provides high clustering performance. In addition, we use simulated data to investigate the interpretability of feature extraction, and visualization results show that the low-dimensional representation learned by ScInfoVAE retains local and global neighborhood structure data well. In addition, our model can significantly improve the quality of the variational posterior.

关键词： Unsupervised clustering Deep neural network variational autoencoder Mutual information scRNA-seq data

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Three Dimensional Microwave Data Inversion in Feature Space for Stroke Imaging

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2024年第4期43卷 1365-1376页

作者： Guo, Rui Lin, Zhichao Xin, Jingyu Li, Maokun Yang, Fan Xu, Shenheng Abubakar, Aria Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Dept Elect Engn Beijing 100084 Peoples R China SLB Houston TX 77056 USA

Microwave imaging is a promising method for early diagnosing and monitoring brain strokes. It is portable, non-invasive, and safe to the human body. Conventional techniques solve for unknown electrical properties represented as pixels or voxels, but often result in inadequate structural information and high computational costs. We propose to reconstruct the three dimensional (3D) electrical properties of the human brain in a feature space, where the unknowns are latent codes of a variational autoencoder (VAE). The decoder of the VAE, with prior knowledge of the brain, acts as a module of data inversion. The codes in the feature space are optimized by minimizing the misfit between measured and simulated data. A dataset of 3D heads characterized by permittivity and conductivity is constructed to train the VAE. Numerical examples show that our method increases structural similarity by 14% and speeds up the solution process by over 3 orders of magnitude using only 4.8% number of the unknowns compared to the voxel-based method. This high-resolution imaging of electrical properties leads to more accurate stroke diagnosis and offers new insights into the study of the human brain.

关键词： Microwave brain imaging stroke imaging deep learning variational autoencoder data inversion

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Detection of wind turbine blade abnormalities through a deep learning model integrating VAE and neural ODE

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OCEAN ENGINEERING 2024年 302卷

作者： Yang, Zeyun Xu, Mingqiang Wang, Shuqing Li, Jun Peng, Zhen Jin, Fei Yang, Yuan Ocean Univ China Coll Engn Qingdao Peoples R China Curtin Univ Ctr Infrastructural Monitoring & Protect Sch Civil & Mech Engn Bentley WA Australia Shanghai Invest Design & Res Inst Co Ltd Shanghai 200335 Peoples R China

This paper introduces a novel deep learning model that integrates a variational autoencoder (VAE) and a neural ordinary differential equation (ODE) for detecting abnormalities in wind turbine blades. The deep learning model is trained using dynamic response data from normally operating blades. Abnormalities are identified when the predicted dynamic responses deviate from the measured ones. Unlike commonly used models like LSTM and GRU that predict time series in the original data space, the proposed model conducts time series prediction in a compressed latent space. The fusion of the VAE and the neural ODE delivers two distinct advantages: (1) the VAE filters out noise and accentuates the underlying structure of data, thereby enhancing prediction accuracy;(2) the time series prediction executed by the neural ODE transpires within the compressed latent space, enabling predictions of extended time series without the need to predict each individual time point directly. The effectiveness of the proposed anomaly detection method is evaluated using both numerical and experimental wind turbine blades. The results demonstrate that the proposed deep learning model outperforms LSTM, GRU, and neural ODE in terms of time series prediction capabilities. Furthermore, the proposed method proves effective in identifying progressive degradation.

关键词： variational autoencoder Neural ordinary differential equation Anomaly detection Wind turbine blade Time series prediction Deep learning

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Open-Set Fault Recognition and Inference for Rolling Bearing Based on Open Fault Semantic Subspace

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Chen, Yu Tao, Laifa Liu, Xue Ma, Jian Lu, Chen Liu, Hongmei Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Intelligent fault diagnosis methods have gained much attention in industry. An important premise of these methods is that the training and test data maintain the same set of fault classes, known as the closed-set hypothesis, which, however, cannot be guaranteed in fault diagnosis tasks. This can result in potentially unknown faults being incorrectly randomly classified as a known fault (KF) class. To overcome this problem, we introduce open-set recognition and proposed an open fault semantic subspace-based open-set fault diagnosis and inference framework (OFS-FDI), which identifies unknown faults while completing the diagnosis of a known class of faults, and furthermore, inferring the possible fault type of unknown samples. First, a fault semantic subspace construction method is proposed to transform the original signal into a set of low-dimensional representation subsets conforming to a conditional Gaussian distribution, which is related to the fault semantic. Then, an outlier score is proposed to determine whether a sample is from an unknown class. Finally, a class directional index (CDI) metric is proposed to perform inferential analysis of possible fault classes. In the experiments based on two typical rotating machines, the outlier detection accuracy of OFS-FDI is improved by up to 0.04% compared with the comparative methods. The accuracy for unknown fault inference (UFI) is up to 99.65%.

关键词： Fault diagnosis open-set recognition rolling bearing unknown fault inference (UFI) variational autoencoder

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Anomaly Signal Imputation Using Latent Coordination Relations

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IEEE ACCESS 2024年 12卷 117072-117089页

作者： Chalongvorachai, Thasorn Woraratpanya, Kuntpong King Mongkuts Inst Technol Ladkrabang Sch Informat Technol Bangkok 10520 Thailand

Missing data is a critical challenge in industrial data analysis, particularly during anomaly incidents caused by system equipment malfunctions or, more critically, by cyberattacks in industrial systems. It impedes effective imputation and compromises data integrity. Existing statistical and machine learning techniques struggle with heavily missing data, often failing to restore original data characteristics. To address this, we propose Anomaly Signal Imputation Using Latent Coordination Relations, a framework employing a variational autoencoder (VAE) to learn from complete data and establish a robust imputation model based on latent space coordination points. Experimental results from a water treatment testbed show significant improvements in output signal fidelity despite substantial data loss, outperforming conventional techniques.

关键词： Imputation Neural networks Nearest neighbor methods Machine learning Accuracy Computer crime Anomaly detection Data analysis Data imputation time series analysis anomaly detection neural networks variational autoencoder latent coordination relations

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A Novel Hybrid Fuzzy-based Deep Convolutional Neural Network for Big-Data-based Hyperspectral Image Classification

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JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY 2024年第5期68卷

作者： Lei, Boyang Kong, Xianguang Yang, Shengkang Dou, Zhenhuan China State Shipbuilding Corp Ltd CSSC Planning & Dev Res Ctr Beijing 100101 Peoples R China Xidian Univ Sch Mechanoelect Engn Xian 710071 Peoples R China Shaanxi Diesel Heavy Ind Co Ltd Xingping 713100 Peoples R China

Hyperspectral imagery holds a significant level of importance as it provides detailed information about various objects owing to the acquisition of narrow-band information. A hyperspectral image encompasses multiple spectral bands and involves intricate processes for the identification and classification of objects manually. The existing hyperspectral image classification methods experience limited spatial resolution and reduced accuracy in the classification process. To overcome this issue, this research article presents a hybrid deep convolutional neural network (HDCNN) for the automatic processing and analysis of hyperspectral images. The HDCNN consists of fuzzy-based convolutional neural networks (FBCNNs) and variational autoencoders (VAEs). Furthermore, non-negative matrix factorization is utilized for the extraction of features and the reduction of dimensionality. In this approach, the FBCNN is employed for the automatic classification of hyperspectral images, taking into account the uncertainty and vagueness present in the data. The VAE is utilized for the detection of anomalies and the generation of new data with meaningful characteristics. Based on the experimental findings, it has been observed that the FBCNN yields enhanced accuracy in classification and exhibits superior performance in terms of accuracy, precision, sensitivity, and recall. The proposed FBCNN exhibits 97.1% of accuracy, 91.47% of sensitivity, 90.86% of precision, and 88.5% of recall.

关键词： hyperspectral images variational autoencoder deep convolutional neural networks non-negative matrix factorization fuzzy-based convolutional neural networks

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Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment

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RESULTS IN ENGINEERING 2025年 25卷

作者： Vijai, Praveen Sivakumar, P. Bagavathi Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Comp Coimbatore India

Anomaly detection is critical for enhancing operational efficiency, safety, and maintenance in industrial applications, particularly in the era of Industry 4.0 and IoT. While traditional anomaly detection approaches face limitations such as scalability issues, high false alarm rates, and reliance on skilled expertise, this study proposes novel approach using a BiLSTM-variational autoencoder (BiLSTM-VAE) model with a dynamic loss function. The proposed model addresses key challenges, including data imbalance, interpretability issues, and computational complexity. By leveraging the bidirectional capability of BiLSTM in the encoder and decoder, the model captures comprehensive temporal dependencies, enabling more effective anomaly detection. The innovative dynamic loss function integrates a tempering index mechanism with tuneable parameters (alpha and gamma), which assigns higher weights to underrepresented classes and down-weights easily classified samples. This improves reconstruction and enhances detection accuracy, particularly for minority class anomalies. Experimental evaluations on the SKAB and TEP datasets demonstrate the superiority of the proposed framework. The model achieved an accuracy of 98% and an F1 score of 96% for binary classification on the SKAB dataset and a multiclass classification accuracy of 92% with an F1 score of 85% on the TEP dataset. These results significantly outperform state-ofthe-art models, including traditional VAE, LSTM, and transformer-based approaches. The proposed BiLSTM-VAE model not only demonstrates robust anomaly detection capabilities across diverse datasets but also effectively handles data imbalance and reduces false positives, making it a scalable and reliable solution for industrial anomaly detection in the context of Industry 4.0 and IoT environments.

关键词： Anomaly detection Deep learning variational autoencoder Bidirectional long short term memory model Dynamic loss function

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Nonlinear Slow Feature Analysis for Oscillating Characteristics Under Deep Encoder-Decoder Framework

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9568-9578页

作者： Puli, Vamsi Krishna Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton T6G 2V4 AB Canada

Slow feature analysis aims to linearly transform measured data into uncorrelated signals that vary from slow to fast. While earlier extensions successfully extracted slow features from nonlinear sequential data, they lacked a modeling preference for nonstationary and oscillating features due to constraints on the prior distribution. To address this limitation, a semisupervised encoder-decoder architecture is proposed in this article, integrating a statistical preference for such characteristics. This regularization is achieved by introducing a first-order autoregressive Gaussian prior within a regular variational auto-encoder framework, as opposed to the standard Gaussian distribution. The evidence lower bound associated with the proposed model is derived using the variational Bayesian inference, and the model parameters are estimated iteratively. The effectiveness of the proposed approach is evaluated on both simulated and real industrial processes.

关键词： Bayesian inference nonstationarity semisupervised learning slow feature analysis soft-sensing variational autoencoder

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Enhanced autoencoder-based fraud detection: a novel approach with noise factor encoding and SMOTE

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第1期66卷 635-652页

作者： Cakir, Mert Yilmaz Sirin, Yahya Istanbul Sabahattin Zaim Univ Comp Sci & Engn TR-34303 Istanbul Turkiye

Fraud detection is a critical task across various domains, requiring accurate identification of fraudulent activities within vast arrays of transactional data. The significant challenges in effectively detecting fraud stem from the inherent class imbalance between normal and fraudulent instances. To address this issue, we propose a novel approach that combines autoencoder-based noise factor encoding (NFE) with the synthetic minority oversampling technique (SMOTE). Our study evaluates the efficacy of this approach using three datasets with severe class imbalance. We compare three autoencoder variants-autoencoder (AE), variational autoencoder (VAE), and contractive autoencoder (CAE)-enhanced by the NFE technique. This technique involves training autoencoder models on real fraud data with an added noise factor during the encoding process, followed by combining this altered data with genuine fraud data. Subsequently, SMOTE is employed for oversampling. Through extensive experimentation, we assess various evaluation metrics. Our results demonstrate the superiority of the autoencoder-based NFE approach over the use of traditional oversampling methods like SMOTE alone. Specifically, the AE-NFE method outperforms other techniques in most cases, although the VAE-NFE and CAE-NFE methods also exhibit promising results in specific scenarios. This study highlights the effectiveness of leveraging autoencoder-based NFE and SMOTE for fraud detection. By addressing class imbalance and enhancing the performance of fraud detection models, our approach enables more accurate identification and prevention of fraudulent activities in real-world applications.

关键词： Fraud detection Noise factor encoding autoencoder variational autoencoder Contractive autoencoder SMOTE

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Learning-Based Image Compression With Parameter-Adaptive Rate-Constrained Loss

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1099-1103页

作者： Guerin Jr, Nilson D. da Silva, Renam Castro Macchiavello, Bruno Univ Brasilia Dept Comp Sci BR-70910900 Brasilia Brazil Univ Brasilia Fac UnB Gama BR-72444240 Gama Leste Brazil

In recent years, the crucial task of image compression has been addressed by end-to-end neural network methods. However, achieving fine-grained rate control in this new paradigm has presented challenges. In our previous work, we explored mismatches in rate estimation during target-rate-oriented training and proposed heuristics involving costly parameter searches as a solution. This work proposes a lightweight approach, which dynamically adapts loss parameters to mitigate rate estimation issues, ensuring precise target rate attainment. Inspired by Reinforcement Learning, our method exhibits performance comparable to preceding approaches on the Kodak dataset in terms of PSNR. Additionally, it reduces computational training costs.

关键词： Convolutional neural network learning-based image compression rate control variational autoencoder

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