Unsupervised Machine Learning

Unsupervised machine learning focuses on extracting patterns and structures from unlabeled data without explicit human guidance, aiming to discover inherent relationships and groupings. Current research emphasizes developing robust algorithms, such as k-means, autoencoders, and variations of graph-based methods like LexRank, to address challenges like the "Clever Hans" effect (where models find spurious correlations) and improve the interpretability of results. This field is crucial for diverse applications, ranging from analyzing complex datasets in astronomy and healthcare to optimizing resource allocation in sustainable development and enhancing the efficiency of various engineering systems.

Papers

April 24, 2023

Unsupervised Machine Learning to Classify the Confinement of Waves in Periodic Superstructures
Marek Kozoň, Rutger Schrijver, Matthias Schlottbom, Jaap J. W. van der Vegt, Willem L. Vos
Data Clustering Unsupervised Machine Learning Clustering Validation Marshak Wave Periodic Structure

February 28, 2023

Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions
Germán González-Almagro, Daniel Peralta, Eli De Poorter, José-Ramón Cano, Salvador García
Clustering Method Research Direction Unsupervised Machine Learning Constrained Clustering Pairwise Constraint

February 20, 2023

Unsupervised OmniMVS: Efficient Omnidirectional Depth Inference via Establishing Pseudo-Stereo Supervision
Zisong Chen, Chunyu Lin, Lang Nie, Kang Liao, Yao Zhao
High Efficiency Panoramic Image Unsupervised Machine Learning Fisheye Image Omnidirectional Depth

January 31, 2023

Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification
Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
Graph Neural Network Semi Supervised Node Classification Unsupervised Machine Learning Neural Implicit Kernel Classification Neighborhood Propagation

November 5, 2022

Unsupervised Machine Learning for Explainable Health Care Fraud Detection
Shubhranshu Shekhar, Jetson Leder-Luis, Leman Akoglu
Service Provider Unsupervised Machine Learning Fraud Pattern Graph Based Fraud Detection Medicare Fraud

September 2, 2022

Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines
Lennart Dabelow, Masahito Ueda
Restricted Boltzmann Machine Unsupervised Machine Learning Accuracy Efficiency Trade Correlation Learning Learning Phase

August 10, 2022

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Siba Moussa, Michael Kilgour, Clara Jans, Alex Hernandez-Garcia, Miroslava Cuperlovic-Culf, Yoshua Bengio, Lena Simine
Unsupervised Machine Learning RNA Design Design Diversity

June 23, 2022

Disentangling representations in Restricted Boltzmann Machines without adversaries
Jorge Fernandez-de-Cossio-Diaz, Simona Cocco, Remi Monasson
Adversarial Training Meaningful Representation Adversary Agent Restricted Boltzmann Machine Unsupervised Machine Learning Adversarial Discriminator Simple Generative

June 17, 2022

ck-means, a novel unsupervised learning method that combines fuzzy and crispy clustering methods to extract intersecting data
Jean-Sébastien Dessureault, Daniel Massicotte
Raw Data Data Clustering Long Form Novel K Mean Clustering Method K Content Unsupervised Machine Learning Learning Method Fuzzy C Mean Fuzzy Clustering

May 19, 2022

Identifying outliers in astronomical images with unsupervised machine learning
Yang Han, Zhiqiang Zou, Nan Li, Yanli Chen
Global Outlier Unsupervised Machine Learning Astronomical Image K Nearest Neighbor Structural Outlier KNN Clip KNN Seq

May 13, 2022

A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data
Nassir Mohammad
Neural Network Anomaly Detection Unsupervised Anomaly Detection Computational Neuroscience Visual Anomaly Detection Unsupervised Machine Learning Unstructured Text Data

May 11, 2022

Unsupervised machine learning for physical concepts
Ruyu Yang
Machine Learning Physical Reasoning Unsupervised Machine Learning Hidden Physic Physical Law

January 7, 2022

December 1, 2021

Unsupervised Statistical Learning for Die Analysis in Ancient Numismatics
Andreas Heinecke, Emanuel Mayer, Abhinav Natarajan, Yoonju Jung
Unsupervised Clustering Unsupervised Machine Learning Common Coin Die Casting Point Cloud

November 4, 2021

Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning
Rik van Leeuwen, Ger Koole
Data Driven Approach Hierarchical Clustering Data Driven Decision Unsupervised Machine Learning

Unsupervised Machine Learning

Papers

Unsupervised Machine Learning to Classify the Confinement of Waves in Periodic Superstructures

Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions

Unsupervised OmniMVS: Efficient Omnidirectional Depth Inference via Establishing Pseudo-Stereo Supervision

Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification

Unsupervised Machine Learning for Explainable Health Care Fraud Detection

Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning

Disentangling representations in Restricted Boltzmann Machines without adversaries

ck-means, a novel unsupervised learning method that combines fuzzy and crispy clustering methods to extract intersecting data

Identifying outliers in astronomical images with unsupervised machine learning

A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data

Unsupervised machine learning for physical concepts

Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

k-Center Clustering with Outliers in Sliding Windows

Unsupervised Statistical Learning for Die Analysis in Ancient Numismatics

Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning