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scSSA: A clustering method for single cell RNA-seq data based on semi-supervised autoencoder

METHODS 2022年 208卷 66-74页

作者： Zhao, Jian-Ping Hou, Tong-Shuai Su, Yansen Zheng, Chun-Hou Xinjiang Univ Coll Math & Syst Sci Urumqi Peoples R China Xinjiang Univ Inst Math & Phys Urumqi Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China

Background: Single cell sequencing is a technology for high-throughput sequencing analysis of genome, tran-scriptome and epigenome at the single cell level. It can improve the shortcomings of traditional methods, reveal the gene structure and gene expression state of a single cell, and reflect the heterogeneity between cells. Among them, the clustering analysis of single-cell RNA data is a very important step, but the clustering of single-cell RNA data is faced with two difficulties, dropout events and dimension curse. At present, many methods are only driven by data, and do not make full use of the existing biological information. Results: In this work, we propose scSSA, a clustering model based on semi-supervised autoencoder, fast inde-pendent component analysis (FastICA) and Gaussian mixture clustering. Firstly, the semi-supervised autoencoder imputes and denoises the scRNA-seq data, and then get the low-dimensional latent representation. Secondly, the low-dimensional representation is reduced the dimension and clustered by FastICA and Gaussian mixture model respectively. Finally, scSSA is compared with Seurat, CIDR and other methods on 10 public scRNA-seq datasets. Conclusion: The results show that scSSA has superior performance in cell clustering on 10 public datasets. In conclusion, scSSA can accurately identify the cell types and is generally applicable to all kinds of single cell datasets. scSSA has great application potential in the field of scRNA-seq data analysis.

关键词： scRNA-seq semi-supervised autoencoder Fast independent component analysis Gaussian mixture clustering

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semi-supervised autoencoder for Chemical Gas Classification with FTIR Spectrum

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SENSORS 2024年第11期24卷 3601页

作者： Jang, Hee-Deok Kwon, Seokjoon Nam, Hyunwoo Chang, Dong Eui Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Agcy Def Dev Adv Def Sci & Technol Res Inst Chem Bio Technol Ctr Daejeon 34186 South Korea

Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.

关键词： chemical warfare agent chemical gas classification Fourier transform infrared deep neural network semi-supervised autoencoder

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semi-supervised autoencoder : A Joint Approach of Representation And Classification 7

Semi-Supervised Autoencoder : A Joint Approach of Representa...

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7th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Wu Haiyan Yang Haomin Li Xueming Ren Haijun Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781509000760

Recent years have witnessed the significant success of representation learning and deep learning in various prediction and recognition applications. Most of these previous studies adopt the two-phase procedures, namely the first step of representation learning and then the second step of supervised learning. In this process, to fit the training data the initial model weights, which inherits the good properties from the representation learning in the first step, will be changed in the second step. In other words, the second step leans better classification models at the cost of the possible deterioration of the effectiveness of representation learning. Motivated by this observation we propose a joint framework of representation and supervised learning. It aims to learn a model, which not only guarantees the "semantics" of the original data from representation learning but also fit the training data well via supervised learning. Along this line we develop the model of semi-supervised autoencoder under the spirit of the joint learning framework. The experiments on various data sets for classification show the significant effectiveness of the proposed model.

关键词： semi-supervised autoencoder deep learning logistic regression Sparse autoencoder

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A semi-supervised autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis

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IEEE ACCESS 2020年 8卷 178295-178310页

作者： Kim, Sunuwe Jo, Soo-Ho Kim, Wongon Park, Jongmin Jeong, Jingyo Han, Yeongmin Kim, Daeil Youn, Byeng Dong Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Korea Elect Power Corp KEPCO Dept Transmiss & Substn Operat Naju 58322 South Korea OnePredict Inc Seoul 08826 South Korea

This paper proposes a semi-supervised autoencoder with an auxiliary task (SAAT) to extract a health feature space for power transformer fault diagnosis using dissolved gas analysis (DGA). The health feature space generated by a semi-supervised autoencoder (SSAE) not only identifies normal and thermal/electrical fault types, but also presents the underlying characteristics of DGA. In the proposed approach, by adding an auxiliary task that detects normal and fault states in the loss function of SSAE, the health feature space additionally enables visualization of health degradation properties. The overall procedure of the new approach includes three key steps: 1) preprocessing DGA data, 2) extracting two health features via SAAT, and 3) visualizing the two health features in two-dimensional space. In this paper, we test the proposed approach using massive unlabeled/labeled Korea Electric Power Corporation (KEPCO) databases and IEC TC 10 databases. To demonstrate the effectiveness of the proposed approach, four comparative studies are conducted with these datasets;the studies examined: 1) the effectiveness of an auxiliary detection task, 2) the effectiveness of the visualization method, 3) conventional fault diagnosis methods, and 4) the state-of-the-art, semi-supervised deep learning algorithms. By examining several evaluation metrics, these comparative studies confirm that the proposed approach outperforms SSAE without the auxiliary task, existing methods, and state-of-the-art deep learning algorithms, in terms of defining health degradation performance. We expect that the proposed SAAT-based health feature space approach will be widely applicable to intuitively monitor the health state of power transformers in the real world.

关键词： Feature extraction Power transformers Fault diagnosis Task analysis Degradation Two dimensional displays Decoding semi-supervised autoencoder health feature space fault diagnosis power transformer dissolved gas analysis

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Ironmaking process modeling uncertainty quantification via conformal prediction based on random vector functional link networks

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Zhou, Ping Wen, Chaoyao Zhao, Peng Li, Mingjie Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Taiyuan Univ Sci & Technol Sch Elect Informat Engn Taiyuan 030024 Peoples R China

For real-world industrial system modeling, dynamic stochastic errors inevitably exist in data- driven deterministic predictions (i.e., point predictions). The uncertainty of such prediction results directly affects various prediction-based operations for work condition identification and production decision-making. Therefore, a novel interval prediction method quantifying multi-output uncertainty is proposed by combining conformal prediction with random vector functional link networks (RVFLNs), which has fast learning speed and high accuracy performance. The proposed algorithm is used for the reliable prediction of molten iron quality in blast furnace ironmaking process. Firstly, to address the issue that shallow learning models have limited expression capabilities to describe complex nonlinear relationships, the dynamic attention mechanism and semi-supervised autoencoder are utilized to reveal and represent the correlations between different input variables and multi-output variables. Subsequently, the Elastic Net regularization technique is adopted to improve the multicollinearity and overfitting problems of traditional RVFLNs. Further, considering the deterioration of prediction accuracy and credibility caused by uncertain system dynamics, an Empirical Copula function-based Copula prediction uncertainty quantification method is introduced to realize multi-output variables reliable prediction with a given confidence level. Finally, actual blast furnace industrial data is applied to demonstrate the validity, utility, and sophistication of model.

关键词： Conformal prediction Random vector functional link networks Uncertainty quantification Blast ironmaking process Quality prediction semi-supervised autoencoder

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FairSwiRL: fair semi-supervised classification with representation learning

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MACHINE LEARNING 2023年第9期112卷 3051-3076页

作者： Yang, Shuyi Cerrato, Mattia Ienco, Dino Pensa, Ruggero G. Esposito, Roberto Univ Turin Dept Comp Sci Turin Italy Johannes Gutenberg Univ Mainz Inst Informat Mainz Germany INRAE Montpellier UMR TETIS LIRMM Montpellier France Intesa Sanpaolo Data Sci & Artificial Intelligence Turin Italy

semi-supervised learning has shown its potential in many real-world applications where only few labeled examples are available. However, when some fairness constraints need to be satisfied, semi-supervised classification models often struggle as they are required to cope with the lack of sufficient information for predicting the target variable while forgetting its relationships with any sensitive and potentially discriminatory attribute. To address this issue, we propose a fair semi-supervised representation learning architecture that leads to fair and accurate classification results even in very challenging scenarios with few labeled (but biased) instances. We show experimentally that our model can be easily adopted in very general settings, as the learned representations may be employed to train any supervised classifier. Moreover, when applied to several synthetic and real-world datasets, our method is competitive with state-of-the-art fair semi-supervised approaches.

关键词： semi-supervised autoencoder Fairness Deep neural networks

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Discriminant autoencoder for feature extraction in fault diagnosis

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 192卷 103814-000页

作者： Luo, Xiaoyi Li, Xianmin Wang, Ziyang Liang, Jun Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Zhejiang Peoples R China State Key Lab Nucl Power Safety Monitoring Techno Shenzhen Guangdong Peoples R China

Nowadays, some traditional autoencoders and their extensions have been widely applied in data-driven fault diagnosis for feature extraction. However, because of the fact that traditional autoencoders could not make use of label information, the representations extracted by these traditional autoencoders may show disappointing results when handling ultimate discriminative task. In this paper, we propose a novel semi-supervised autoencoder, which is named as Discriminant autoencoder. The training of proposed Discriminant autoencoder includes a supervised process and an unsupervised process. And a distance penalty is added into the loss function, which enables the proposed Discriminant autoencoder to extract more suitable representations from industrial data samples. In order to explain the effectiveness of this semi-supervised autoencoder, we carry out some experiments and give out a mathematical derivation. Here we use an industrial batch process dataset as the criterion dataset to test the performance of proposed Discriminant autoencoder and other conventional autoencoders.

关键词： Fault diagnosis Feature extraction autoencoder semi-supervised autoencoder

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Integrating Multi-Network Topology via Deep semi-supervised Node Embedding 19

Integrating Multi-Network Topology via Deep Semi-supervised ...

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28th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Xue, Hansheng Peng, Jiajie Li, Jiying Shang, Xuequn Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781450369763

Node Embedding, which uses low-dimensional non-linear feature vectors to represent nodes in the network, has shown a great promise, not only because it is easy-to-use for downstream tasks, but also because it has achieved great success on many network analysis tasks. One of the challenges has been how to develop a node embedding method for integrating topological information from multiple networks. To address this critical problem, we propose a novel node embedding, called DeepMNE, for multi-network integration using a deep semi-supervised autoencoder. The key point of DeepMNE is that it captures complex topological structures of multiple networks and utilizes correlation among multiple networks as constraints. We evaluate DeepMNE in node classification task and link prediction task on four real-world datasets. The experimental results demonstrate that DeepMNE shows superior performance over seven state-of-the-art single-network and multi-network embedding algorithms.

关键词： Node Embedding Multi-Network Representation Learning semi-supervised autoencoder Network Constraints

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