Extremal region selection for MSER detection in food recognition by Razali, Mohd Norhisham, Manshor, Noridayu, Abdul Halin, Alfian, Mustapha, Norwati, Yaakob, Razali

“…Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. The performance of ERS algorithm is evaluated based on the classification performance metrics by using classification rate (CR), error rate (ERT), precision (Prec.) and recall (rec.) as well as the number of extremal regions produced by ERS. …”
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Multilevel learning in Kohonen SOM network for classification problems by Mohd. Yusof, Norfadzila

“…Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. …”
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Clustering Student Performance Data Using k-Means Algorithms by Sultan Alalawi, Sultan Juma, Mohd Shaharanee, Izwan Nizal, Mohd Jamil, Jastini

“…Clustering, an unsupervised learning technique, is one of the most powerful machine- learning tools for discovering patterns and unseen data. …”
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Time-series classification vegetables in detecting growth rate using machine learning by Ezahan Hilmi, Zakaria, Mohd Azraai, Mohd Razman, Jessnor Arif, Mat Jizat, Ismail, Mohd Khairuddin, Zelina Zaiton, Ibrahim, Anwar, P. P. Abdul Majeed

“…This paper presents a clustering of unsupervised learning based innovative system to forecast the irrigation requirements of a field using the sensing of a ground parameter such as soil moisture, light intensity, temperature, and humidity. …”
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Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis by Mohd Izzat, Mohd Rahman, Mohd Azraai, Mohd Razman, Abdul Majeed, Anwar P. P., Muhammad Nur Aiman, Shapiee, Muhammad Amirul, Abdullah, Musa, Rabiu Muazu

“…The system uses unsupervised learning-based clustering to predict the irrigation needs of a field based on the ground parameters sensed by automated monitoring devices. …”
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Sensor placement strategy to inform decisions by Willcox, Karen E, Mainini, Laura

“…This paper introduces a computational strategy to determine optimal sets of sensor locations to support real-time operational decisions. We exploit unsupervised learning strategies (specifically self-organizing maps) to identify the most informative locations to place sensors. …”
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Deciphering Neural Codes of Memory during Sleep by Chen, Zhe, Wilson, Matthew A.

“…We focus on two analysis paradigms for sleep-associated memory and propose a new unsupervised learning framework (‘memory first, meaning later’) for unbiased assessment of SANCs. …”
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On memories, neural ensembles and mental flexibility by Pinotsis, Dimitris A., Brincat, Scott Louis, Miller, Earl K

“…It also links the oscillatory frequencies observed in ensembles with the spatial scales at which activity is expressed. Using unsupervised learning, biophysical modeling and graph theory, we analyze multi-electrode LFPs from frontal cortex during a spatial delayed response task. …”
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Deep long short-term memory networks for nonlinear structural seismic response prediction by Buyukozturk, Oral, Sun, Hao

“…In addition, an unsupervised learning algorithm based on a proposed dynamic K-means clustering approach is established to cluster the seismic inputs in order to (1) generate the least but the most informative datasets for training the LSTM and (2) improve the prediction accuracy and robustness of the model trained with limited data. …”
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Unsupervised phase learning and extraction from repetitive movements by Jatesiktat, Prayook, Anopas, Dollaporn, Ang, Wei Tech

“…To make it more universal, a novel unsupervised-learning-based phase extraction technique is proposed. …”
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Deep image restoration and enhancement by Fu, Zixuan

“…To solve this problem, this thesis considers a more general and practical unsupervised-learning setting for image denoising, which is achieving image denoising by utilizing unpaired noisy and clean images. …”
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Clustering via adaptive and locality-constrained graph learning and unsupervised ELM by Zeng, Yijie, Chen, Jichao, Li, Yue, Qing, Yuanyuan, Huang, Guang-Bin

“…We demonstrate the importance of locality by generalizing the Locality-constrained Linear Coding (LLC) for unsupervised learning. Each data sample is expressed as a representation of its nearest neighbors, which naturally leads to a combination of distance regularized features and a Locally Linear Embedding (LLE) decomposition. …”
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Spatial data clustering with boundary detection by Liu, Dong Quan

“…It is also named unsupervised learning in artificial intelligence (AI) research field. …”
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Probabilistic models and Monte Carlo objectives in generative modelling by Shi, Y

“…Recently, the denoising diffusion model (DDM) has emerged as another state-of-the-art generative model, which can also be viewed under the LVM framework but provides even higher expressivity and quality of learning and achieved exceptional results in many tasks such as unsupervised learning of images.</p> <p>In this thesis, we present a variety of advances in theory and methodology for LVM and DDM, and explore a novel class of generative models named diffusion Schrödinger bridge (DSB). …”

Linked independent component analysis for multimodal data fusion. by Groves, A, Beckmann, C, Smith, S, Woolrich, M

“…Independent Component Analysis (ICA) is a popular unsupervised learning method that can be used to find the modes of variation in neuroimaging data across a group of subjects. …”

An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets by Azlin, Ahmad, Rubiyah, Yusof, Nor Saradatul Akmar, Zulkifli, Mohd Najib, Ismail

“…However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervised learning process. The inadequate amount of class label data in a dataset significantly affects the clustering learning process, leading to inefficient and unreliable results. …”
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Music Recommender System Using Machine Learning Content-Based Filtering Technique by Foong, Kin Hong

“…These are the popular algorithm for unsupervised learning, a machine learning method to analyse and cluster datasets. …”
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Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions by Luders, Brandon Douglas, Ferguson, Sarah K., Grande, Robert Conlin, How, Jonathan P

“…This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. …”
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Automated Sub-Zoning of Water Distribution Systems by Allen, Michael, Preis, Ami, Iqbal, Mudasser, Perelman, Lina Sela, Whittle, Andrew

“…This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Global clustering – a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure – a bottom-up algorithm based on the property of network modularity, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning – a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. …”
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Ambient Sound Provides Supervision for Visual Learning by Owens, Andrew Hale, Wu, Jiajun, McDermott, Joshua H., Freeman, William T., Torralba, Antonio

“…We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.…”
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