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Journal articles on the topic 'Unsupervied learning'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Fong, A. C. M., and G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning." International Journal of Machine Learning and Computing 11, no. 2 (2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.

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Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data. This is especially true for deep learning (DL) neural networks, which need huge annotated data sets for good performance. However, large volumes of annotated data are not always readily available. In addition, some of the best performing ML and DL algorithms lack explainability – it is often difficult even for domain experts to interpret the results. This is an important consideration especially in safety-critical applications, such as AI-assisted medical endeavors, in which a DL’s failure

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2

Xu, Mingle, Sook Yoon, Jaesu Lee, and Dong Sun Park. "Unsupervised Transfer Learning for Plant Anomaly Recognition." Korean Institute of Smart Media 11, no. 4 (2022): 30–37. http://dx.doi.org/10.30693/smj.2022.11.4.30.

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Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this

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Hu Haofeng, 胡浩丰, 金慧烽 Jin Huifeng, 李校博 Li Xiaobo, 翟京生 Zhai Jingsheng та 刘铁根 Liu Tiegen. "基于无监督学习的偏振图像去噪方法". Acta Optica Sinica 43, № 4 (2023): 0410001. http://dx.doi.org/10.3788/aos221645.

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4

A Sowe, Ebou. "Momentum Contrast for Unsupervised Visual Representation Learning." Journal of Advances in Civil and Mechanical Engineering 2, no. 1 (2025): 01–06. https://doi.org/10.64030/3067-2457.02.01.02.

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This brief report presents a novel unsupervised learning representation learning method called momentum contrast. Momentum contrast uses a contrastive learning technique to learn representations by comparing features of related yet dissimilar images for efficient feature extraction and unsupervised representation learning. Similar images are grouped together, and dissimilar images are placed far apart. The method builds upon previous works in contrastive learning but includes a momentum optimisation step to improve representation learning performance and generate better quality representations

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Kruglov, Artem V. "The Unsupervised Learning Algorithm for Detecting Ellipsoid Objects." International Journal of Machine Learning and Computing 9, no. 3 (2019): 255–60. http://dx.doi.org/10.18178/ijmlc.2019.9.3.795.

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6

Yu-Dong Cao, Yu-Dong Cao, Shuang-Jiang Hang Yu-Dong Cao, and Xu Jia Shuang-Jiang Hang. "Improving Unsupervised Domain Adaptation via Multiple Adversarial Learning." 電腦學刊 34, no. 5 (2023): 073–85. http://dx.doi.org/10.53106/199115992023103405006.

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<p>Most machine learning methods assume the training and test sets to be independent and have identical distributions. However, this assumption does not always hold true in practical applications. Direct training usually induces poor performance if the training and test data have distribution shifts. To address this issue, a three-part model based on using a feature extractor, a classifier, and several domain discriminators is adopted herein. This unsupervised domain adaptation model is based on multiple adversarial learning with samples of different importance. A deep neural network is

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7

Shi, Chengming, Bo Luo, Hongqi Li, Bin Li, Xinyong Mao, and Fangyu Peng. "Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring." International Journal of Machine Learning and Computing 6, no. 5 (2016): 256–59. http://dx.doi.org/10.18178/ijmlc.2016.6.5.607.

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8

S Thakare Jayshri, Vishal. "An Effective Unsupervised Machine Learning Technique and Research Challenges." International Journal of Science and Research (IJSR) 12, no. 5 (2023): 2141–43. http://dx.doi.org/10.21275/sr23523214829.

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d

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Barlow, H. B. "Unsupervised Learning." Neural Computation 1, no. 3 (1989): 295–311. http://dx.doi.org/10.1162/neco.1989.1.3.295.

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What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the cognitive maps and working models of Tolman and Craik, and the idea that redundancy is important for understanding perception (Attneave 1954), the physiology of sensory pathways (Barlow 1959), and pattern recognition (Watanabe 1960). It is argued that (1) The redundancy of sensory messages provides the knowledge incorporated in the maps or models.

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11

Valkenborg, Dirk, Axel-Jan Rousseau, Melvin Geubbelmans, and Tomasz Burzykowski. "Unsupervised learning." American Journal of Orthodontics and Dentofacial Orthopedics 163, no. 6 (2023): 877–82. http://dx.doi.org/10.1016/j.ajodo.2023.04.001.

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12

Banzi, Jamal, Isack Bulugu, and Zhongfu Ye. "Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation." International Journal of Machine Learning and Computing 9, no. 4 (2019): 432–39. http://dx.doi.org/10.18178/ijmlc.2019.9.4.822.

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13

Wu Guangyi, 吴广义, 袁卓群 Yuan Zhuoqun та 梁艳梅 Liang Yanmei. "基于深度学习的视网膜OCT图像无监督去噪方法". Acta Optica Sinica 43, № 20 (2023): 2010002. http://dx.doi.org/10.3788/aos230720.

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14

Jha, Ritambhara. "Analyzing Credit Card Consumer Behavior using Unsupervised Machine Learning Techniques." International Journal of Science and Research (IJSR) 13, no. 1 (2024): 460–63. http://dx.doi.org/10.21275/sr24106025150.

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15

S Nair, Aparna, and Sindhu Daniel. "Customer Segmentation Using K-Means Clustering in Unsupervised Machine Learning." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1376–79. https://doi.org/10.21275/sr25417125301.

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16

Mounika, D. Venkata, and Mr K. Padmanaban. "Unsupervised Machine Learning For Managing Safety Accidents In Railway Stations." International Journal of Research Publication and Reviews 6, no. 5 (2025): 12240–50. https://doi.org/10.55248/gengpi.6.0525.18149.

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17

Watkin, T. L. H., and J. P. Nadal. "Optimal unsupervised learning." Journal of Physics A: Mathematical and General 27, no. 6 (1994): 1899–915. http://dx.doi.org/10.1088/0305-4470/27/6/016.

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18

Sanger, T. "Optimal unsupervised learning." Neural Networks 1 (January 1988): 127. http://dx.doi.org/10.1016/0893-6080(88)90166-9.

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19

Zhuo Wang, Zhuo Wang, Min Huang Zhuo Wang, Xiao-Long Huang Min Huang, Fei Man Xiao-Long Huang, Jia-Ming Dou Fei Man, and Jian-li Lyu Jia-Ming Dou. "Unsupervised Learning of Depth and Ego-Motion from Continuous Monocular Images." 電腦學刊 32, no. 6 (2021): 038–51. http://dx.doi.org/10.53106/199115992021123206004.

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20

Arokiaraj, Christian St Hubert, Vimalesh R., Ranjith M., and Aravind Raj S. "Predicting Credit Card Approval of Customers Through Customer Profiling using Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 52–557. https://doi.org/10.35940/ijeat.D7293.049420.

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In the banking sector, every banking infrastructure contains an enormous dataset for customers’ credit card approval which requires customer profiling. The customer profiling means collection of data related to what customers need. It depends on customers’ basic information like field of work, address proof, credit score, salary details, etc. This process mainly concentrates on predicting approval of credit cards to customers using machine learning. Machine Learning is the scientific study of algorithms and statistical models that computers use to perform specific tasks without any

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21

Mamun, Abdullah Al, Md Shakhaowat Hossain, S. M. Shadul Islam Rishad, et al. "MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS." American Journal of Engineering and Technology 06, no. 11 (2024): 63–76. https://doi.org/10.37547/tajet/volume06issue11-08.

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This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key

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22

Hossain, Md Shakhaowat, S. M. Shadul Islam Rishad, Md Mohibur Rahman, et al. "MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS." International journal of networks and security 04, no. 01 (2024): 22–32. http://dx.doi.org/10.55640/ijns-04-01-06.

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This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key

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23

Chen Guoyang, 陈国洋, 吴小俊 Wu Xiaojun та 徐天阳 Xu Tianyang. "基于深度学习的无监督红外图像与可见光图像融合算法". Laser & Optoelectronics Progress 59, № 4 (2022): 0410010. http://dx.doi.org/10.3788/lop202259.0410010.

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24

Xuejun Zhang, Xuejun Zhang, Jiyang Gai Xuejun Zhang, Zhili Ma Jiyang Gai, et al. "Exploring Unsupervised Learning with Clustering and Deep Autoencoder to Detect DDoS Attack." 電腦學刊 33, no. 4 (2022): 029–44. http://dx.doi.org/10.53106/199115992022083304003.

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<p>With the proliferation of services available on the Internet, network attacks have become one of the seri-ous issues. The distributed denial of service (DDoS) attack is such a devastating attack, which poses an enormous threat to network communication and applications and easily disrupts services. To defense against DDoS attacks effectively, this paper proposes a novel DDoS attack detection method that trains detection models in an unsupervised learning manner using preprocessed and unlabeled normal network traffic data, which can not only avoid the impact of unbalanced training data

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25

Wen-Jen Ho, Wen-Jen Ho, Hsin-Yuan Hsieh Wen-Jen Ho, and Chia-Wei Tsai Hsin-Yuan Hsieh. "Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning." 網際網路技術學刊 25, no. 3 (2024): 455–63. http://dx.doi.org/10.53106/160792642024052503011.

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<p>In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electri

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26

Liu Kang, 刘康, 孙熊伟 Sun Xiongwei, 施海亮 Shi Hailiang та ін. "基于无监督学习的风洞压敏漆图像配准算法". Acta Optica Sinica 44, № 9 (2024): 0915002. http://dx.doi.org/10.3788/aos231885.

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27

Li, Changsheng, Kaihang Mao, Lingyan Liang, et al. "Unsupervised Active Learning via Subspace Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8332–39. http://dx.doi.org/10.1609/aaai.v35i9.17013.

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Unsupervised active learning has been an active research topic in machine learning community, with the purpose of choosing representative samples to be labelled in an unsupervised manner. Previous works usually take the minimization of data reconstruction loss as the criterion to select representative samples which can better approximate original inputs. However, data are often drawn from low-dimensional subspaces embedded in an arbitrary high-dimensional space in many scenarios, thus it might severely bring in noise if attempting to precisely reconstruct all entries of one observation, leadin

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28

He, Shuncheng, Yuhang Jiang, Hongchang Zhang, Jianzhun Shao, and Xiangyang Ji. "Wasserstein Unsupervised Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6884–92. http://dx.doi.org/10.1609/aaai.v36i6.20645.

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Unsupervised reinforcement learning aims to train agents to learn a handful of policies or skills in environments without external reward. These pre-trained policies can accelerate learning when endowed with external reward, and can also be used as primitive options in hierarchical reinforcement learning. Conventional approaches of unsupervised skill discovery feed a latent variable to the agent and shed its empowerment on agent’s behavior by mutual information (MI) maximization. However, the policies learned by MI-based methods cannot sufficiently explore the state space, despite they can be

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29

Kosko, B. "Unsupervised learning in noise." IEEE Transactions on Neural Networks 1, no. 1 (1990): 44–57. http://dx.doi.org/10.1109/72.80204.

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30

Hentschel, H. G. E., and Z. Jiang. "Prediction using unsupervised learning." Physica D: Nonlinear Phenomena 67, no. 1-3 (1993): 151–65. http://dx.doi.org/10.1016/0167-2789(93)90203-d.

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31

Hammarström, Harald, and Lars Borin. "Unsupervised Learning of Morphology." Computational Linguistics 37, no. 2 (2011): 309–50. http://dx.doi.org/10.1162/coli_a_00050.

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This article surveys work on Unsupervised Learning of Morphology. We define Unsupervised Learning of Morphology as the problem of inducing a description (of some kind, even if only morpheme-segmentation) of how orthographic words are built up given only raw text data of a language. We briefly go through the history and motivation of the this problem. Next, over 200 items of work are listed with a brief characterization, and the most important ideas in the field are critically discussed. We summarize the achievements so far and give pointers for future developments.

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32

Reimann, P. "Unsupervised learning of distributions." Europhysics Letters (EPL) 40, no. 3 (1997): 251–56. http://dx.doi.org/10.1209/epl/i1997-00456-2.

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33

Zhang, Fengda, Kun Kuang, Long Chen, et al. "Federated unsupervised representation learning." Frontiers of Information Technology & Electronic Engineering 24, no. 8 (2023): 1181–93. http://dx.doi.org/10.1631/fitee.2200268.

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34

M., Pavithra, Nandhini P., and Suganya R. "A Research on Different Clustering Algorithms and Techniques." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 505–11. https://doi.org/10.31142/ijtsrd15899.

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Learning is the process of generating useful information from a huge volume of data. Learning can be classified as supervised learning and unsupervised learning. Clustering is a kind of unsupervised learning. Clustering is also one of the data mining methods. In all clustering algorithms, the goal is to minimize intracluster distances, and to maximize intercluster distances. Whatever a clustering algorithm provides a better performance, it has the more successful to achieve this goal 2 . Nowadays, although many research done in the field of clustering algorithms, these algorithms have the chal

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35

Ridwan, Ishola Bayo. "Transforming Customer Segmentation with Unsupervised Learning Models and Behavioral Data in Digital Commerce." International Journal of Research Publication and Reviews 6, no. 5 (2025): 2232–49. https://doi.org/10.55248/gengpi.6.0525.1652.

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36

Mežnar, Sebastian, Nada Lavrač, and Blaž Škrlj. "SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations." IEEE Access 8 (December 9, 2020): 212568–88. https://doi.org/10.1109/ACCESS.2020.3039541.

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Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations. However, state-of-the-art methods are not necessarily interpretable and are therefore not fully applicable to sensitive settings in biomedical or user profiling tasks, where explicit bias detection is highly relevant. The proposed SNoRe (Symbolic Node Representations) algorithm is capable of learning symbolic, human-understandable representations of individual network nodes, based on the similarity of neighborhood hashes which serv

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37

Sibyan, Hidayatus, Wildan Suharso, Edi Suharto, Melda Agnes Manuhutu, and Agus Perdana Windarto. "Optimization of Unsupervised Learning in Machine Learning." Journal of Physics: Conference Series 1783, no. 1 (2021): 012034. http://dx.doi.org/10.1088/1742-6596/1783/1/012034.

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38

Xu, Hui, Jiaxing Wang, Hao Li, Deqiang Ouyang, and Jie Shao. "Unsupervised meta-learning for few-shot learning." Pattern Recognition 116 (August 2021): 107951. http://dx.doi.org/10.1016/j.patcog.2021.107951.

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39

Sharma, Ritu. "Study of Supervised Learning and Unsupervised Learning." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (2020): 588–93. http://dx.doi.org/10.22214/ijraset.2020.6095.

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40

Xu, Zhengjun, Detian Huang, Xiaoqian Huang, Jiaxun Song, and Hang Liu. "DLUT: Decoupled Learning-Based Unsupervised Tracker." Sensors 24, no. 1 (2023): 83. http://dx.doi.org/10.3390/s24010083.

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Unsupervised learning has shown immense potential in object tracking, where accurate classification and regression are crucial for unsupervised trackers. However, the classification and regression branches of most unsupervised trackers calculate object similarities by sharing cross-correlation modules. This leads to high coupling between different branches, thus hindering the network performance. To address the above issue, we propose a Decoupled Learning-based Unsupervised Tracker (DLUT). Specifically, we separate the training pipelines of different branches to unlock their inherent learning

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41

Pothos, Emmanuel M., and Nick Chater. "Unsupervised Categorization and Category Learning." Quarterly Journal of Experimental Psychology Section A 58, no. 4 (2005): 733–52. http://dx.doi.org/10.1080/02724980443000322.

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When people categorize a set of items in a certain way they often change their perceptions for these items so that they become more compatible with the learned categorization. In two experiments we examined whether such changes are extensive enough to change the unsupervised categorization for the items—that is, the categorization of the items that is considered more intuitive or natural without any learning. In Experiment 1 we directly employed an unsupervised categorization task; in Experiment 2 we collected similarity ratings for the items and inferred unsupervised categorizations using Pot

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42

Laxmi, Gautam, and Kumar Rajneesh. "Trajectory Data to Improve Unsupervised Learning and Intrinsic." Applied Science and Biotechnology Journal for Advanced Research 3, no. 1 (2024): 16–20. https://doi.org/10.5281/zenodo.10656240.

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The three primary components of machine learning (ML) are reinforcement learning, unstructured learning, and structured learning. The last level, reinforcement learning, will be the main topic of this study. We'll cover a few of the more well-liked reinforcement learning techniques, though there are many more. Reinforcement agents are software agents that make use of reinforcement learning to optimize their rewards within a specific context. The two primary categories of rewards are extrinsic and intrinsic. It's a certain result we obtain after abiding by a set of guidelines and achieving a pa

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Amrita, Sadarangani *. Dr. Anjali Jivani. "A SURVEY OF SEMI-SUPERVISED LEARNING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 10 (2016): 138–43. https://doi.org/10.5281/zenodo.159333.

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Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classification, and unsupervised clustering and semi-supervised clustering are also discussed.

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Chua, Sook-Ling, Stephen Marsland, and Hans Guesgen. "Unsupervised Learning of Human Behaviours." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 319–24. http://dx.doi.org/10.1609/aaai.v25i1.7911.

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Behaviour recognition is the process of inferring the behaviour of an individual from a series of observations acquired from sensors such as in a smart home. The majority of existing behaviour recognition systems are based on supervised learning algorithms, which means that training them requires a preprocessed, annotated dataset. Unfortunately, annotating a dataset is a rather tedious process and one that is prone to error. In this paper we suggest a way to identify structure in the data based on text compression and the edit distance between words, without any prior labelling. We demonstrate

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45

Mo, Yujie, Liang Peng, Jie Xu, Xiaoshuang Shi, and Xiaofeng Zhu. "Simple Unsupervised Graph Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7797–805. http://dx.doi.org/10.1609/aaai.v36i7.20748.

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In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the inter-class variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchor embeddings for reducing the intra-class variation. As a result, both enlarging inter-class variation and reducing intra-class variation result in small generalization error,

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Lotfi, Ismail, Lamiae Megzari, and Abdelhamid Bouhadi. "Asset allocation by Unsupervised Learning." Review of Economics and Finance 19 (2021): 338–46. http://dx.doi.org/10.55365/1923.x2021.19.34.

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47

Zhao, Tingting, Zifeng Wang, Aria Masoomi, and Jennifer Dy. "Deep Bayesian Unsupervised Lifelong Learning." Neural Networks 149 (May 2022): 95–106. http://dx.doi.org/10.1016/j.neunet.2022.02.001.

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48

Martínez-Toro, Gabriel Mauricio, Dewar Rico-Bautista, Efrén Romero-Riaño, and Paola Andrea Romero-Riaño. "Unsupervised learning: application to epilepsy." Revista Colombiana de Computación 20, no. 2 (2019): 20–27. http://dx.doi.org/10.29375/25392115.3718.

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Epilepsy is a neurological disorder characterized by recurrent seizures. The primary objective is to present an analysis of the results shown in the training data simulation charts. Data were collected by means of the 10-20 system. The “10–20” system is an internationally recognized method to describe and apply the location of scalp electrodes in the context of an EEG exam. It shows the differences obtained between the tests generated and the anomalies of the test data based on training data. Finally, the results are interpreted and the efficacy of the procedure is discussed.

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Gao, Jiabao, Caijun Zhong, Xiaoming Chen, Hai Lin, and Zhaoyang Zhang. "Unsupervised Learning for Passive Beamforming." IEEE Communications Letters 24, no. 5 (2020): 1052–56. http://dx.doi.org/10.1109/lcomm.2020.2965532.

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Fan, Qingnan, Jiaolong Yang, David Wipf, Baoquan Chen, and Xin Tong. "Image smoothing via unsupervised learning." ACM Transactions on Graphics 37, no. 6 (2019): 1–14. http://dx.doi.org/10.1145/3272127.3275081.

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