Learning invariant representations and applications to face verification by Liao, Qianli, Leibo, Joel Z., Poggio, Tomaso A.

“…One approach to computer object recognition and modeling the brain's ventral stream involves unsupervised learning of representations that are invariant to common transformations. …”
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Understanding campus mobility by Toh, Christopher Gerard Wei Hong

“…With these findings, this project looks to propose several next steps – such as using this analysis to make better management decisions, and using unsupervised learning techniques for group identification – which could take the analysis of human mobility to the next level, building on the work that has already been done.…”
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Optimizing multistage discriminative dictionaries for blind image quality assessment by Jiang, Qiuping, Shao, Feng, Lin, Weisi, Gu, Ke, Jiang, Gangyi, Sun, Huifang

“…Second, there is a semantic gap between the constructed codebook by unsupervised learning and image quality. To address these problems, we propose a novel codebook-based BIQA method by optimizing multistage discriminative dictionaries (MSDDs). …”
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Time series clustering and characterization by Lie, Rhys

“…While forecasting predicts future trends based on historical data, clustering plays a pivotal role in pre-processing, grouping time series into homogenous clusters based on their temporal trends and underlying characteristics. This unsupervised learning task provides valuable insights for pattern discovery, anomaly detection, and data organization. …”
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Temporal output discrepancy for loss estimation-based active learning by Huang, Siyu, Wang, Tianyang, Xiong, Haoyi, Wen, Bihan, Huan, Jun, Dou, Dejing

“…On basis of TOD, we further develop an effective unlabeled data sampling strategy as well as an unsupervised learning criterion for active learning. Due to the simplicity of TOD, our methods are efficient, flexible, and task-agnostic. …”
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TSception: capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition by Ding, Yi, Robinson, Neethu, Zhang, Su, Zeng, Qiuhao, Guan, Cuntai

“…The performance of the proposed network is compared with prior reported methods such as SVM, KNN, FBFgMDM, FBTSC, Unsupervised learning, DeepConvNet, ShallowConvNet, and EEGNet. …”
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Data-driven phase extraction for anomaly detection of repetitive human movements by Jatesiktat, Prayook

“…To make the method invariant to the variation in phase progression, a neural network is trained to extract phases from unlabeled time-series of kinematic features. This unsupervised learning method enhanced the generalization of the modelling process to be applied to a wide range of repetitive exercises without the need for handcrafting new features. …”
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Structure-aware machine learning over multi-relational databases by Schleich, MJ

“…This class of models includes supervised machine learning problems for regression and classification, as well as unsupervised learning problems.</p> <p>This theoretical development informed the design and implementation of LMFAO (Layered Multiple Functional Aggregate Optimization), an in-memory optimization and execution engine for batches of aggregates over the input database. …”

Multi-omics analysis of adipogenesis by Arroyo, V

“…Here, two classes of unsupervised learning methods were used to investigate which of these phenotypes were most strongly associated with differences in omics data between human adipocytes. …”

Bayesian learning methods for modelling functional MRI by Groves, A

“…</p> <p>The same framework is used for unsupervised learning by placing independent component analysis (ICA) priors on the spatial maps. …”

Extremal Region Selection for MSER Detection in Food Recognition by Mohd Norhisham Razali @ Ghazali, Noridayu Manshor, Alfian Abdul Halin, Norwati Mustapha, Razali Yaakob

“…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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An improved algorithm for iris classification by using support vector machine and binary random machine learning by Kamarulzalis, Ahmad Haadzal

“…Those branch are consist of supervised learning, unsupervised learning and reinforcement learning. This study focuses on supervised learning that seek to classify all the Iris dataset respect to three species (setosa, versicolor and virginica) in order them to mimic the actual dataset by using Support Vector Machine with four different kernel function (Linear, Radial Basis, Sigmoid and Polynomial), Random Forest (RF), k-Nearest Neighbors(k-NN) and Random Nearest Neighbors (RNN) as a method. …”
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An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification by Sulaiman, Dawlat Mustafa, Abdulazeez, Adnan Mohsin, Zebari, Dilovan Asaad, Zeebaree, Diyar Qader, Mostafa, Salama A., Saleem Sadiq, Shereen

“…Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. …”
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An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification by Sulaiman, Dawlat Mustafa, Abdulazeez, Adnan Mohsin, Asaad Zebar, Dilovan, Zeebaree, Diyar Qader, Mostafa, Salama A., Saleem Sadiq, Shereen

“…Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. …”
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An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification by Mustafa Sulaiman, Dawlat, Abdulazeez, Adnan Mohsin, Asaad Zebari, Dilovan, Qader Zeebaree, Diyar, A. Mostaf, Salama, Saleem Sadiq, Shereen

“…Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. …”
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An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification by Sulaiman, Dawlat Mustafa, Abdulazeez, Adnan Mohsin, Asaad Zebar, Dilovan, Diyar Qader Zeebaree, Diyar Qader Zeebaree, A. Mostafa, Salama, Saleem Sadiq, Shereen

“…Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. …”
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Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters by Ahmad Fakhri, Ab. Nasir, Siti Suhaila, Sabarudin, Anwar, P. P. Abdul Majeed, Ahmad Shahrizan, Abdul Ghani

“…Two methods, namely, supervised learning (using weight measure as graded by egg supplier) and unsupervised learning (using egg shape parameters as graded by ourselves), are conducted to execute the experiment. …”
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Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches by Tusher, Ekramul Haque, Mohd Arfian, Ismail, Khan, Ferose, Anis Farihan, Mat Raffei, Md Akbar, Jalal Uddin

“…In order to improve the accuracy of the CVD prediction system, a wide variety of supervised and unsupervised learning approaches from the fields of machine learning were used. …”
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Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks by Shah, Julie A, Nikolaidis, Stefanos, Ramakrishnan, Ramya, Gu, Keren

“…First, the demonstrated action sequences are clustered into different human types using an unsupervised learning algorithm. A reward function is then learned for each type through the employment of an inverse reinforcement learning algorithm. …”
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Single image intrinsic decomposition without a single intrinsic image by Ma, Wei-Chiu, Chu, Hang, Zhou, Bolei, Urtasun, Raquel, Torralba, Antonio

“…. ©2018 Keywords: intrinsic decomposition; unsupervised learning; self-supervised learning…”
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