Unsupervised Learning for High-Dimensional Data: Advancements in Unsupervised Learning Techniques like Clustering, Anomaly Detection, and Dimensionality Reduction

Gaurav Kashyap

doi:10.5281/zenodo.14866681

Unsupervised Learning for High-Dimensional Data: Advancements in Unsupervised Learning Techniques like Clustering, Anomaly Detection, and Dimensionality Reduction

Author(s)	Gaurav Kashyap
Country	United States
Abstract	In the field of machine learning, unsupervised learning has become an essential tool for analyzing high-dimensional, complex data. Unlike supervised learning, which relies on labeled data for model training, unsupervised learning methods aim to identify hidden patterns and structures in unlabeled data. Recent developments in unsupervised learning methods are examined in this paper, with an emphasis on dimensionality reduction, anomaly detection, and clustering. These methods are essential for effective data analysis because high-dimensional data, sometimes known as the "curse of dimensionality," poses serious difficulties for conventional machine learning algorithms. We go over the development of these techniques, their uses, difficulties, and the most recent advancements in the field of high-dimensional data handling. In the age of high-dimensional data, unsupervised learning techniques have grown in importance. This study explores the developments in dimensionality reduction, anomaly detection, and clustering techniques for intricate, high-dimensional datasets. It examines the unique challenges posed by such data and the innovative approaches that have emerged to tackle them.
Keywords	Unsupervised Learning, High-Dimensional Data, Clustering, Anomaly Detection, Dimensionality Reduction, Curse of Dimensionality, Pattern Recognition, Data Analysis, Feature, Extraction, Autoencoders, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor, Embedding (t-SNE), Manifold Learning, Deep Learning, Data Visualization, Subspace Clustering, Outlier, Detection, Nonlinear Dimensionality Reduction, Spectral Clustering, Local Outlier Factor (LOF), Graph Convolutional Networks (GCN)
Field	Engineering
Published In	Volume 5, Issue 12, December 2024
Published On	2024-12-04
Cite This	Unsupervised Learning for High-Dimensional Data: Advancements in Unsupervised Learning Techniques like Clustering, Anomaly Detection, and Dimensionality Reduction - Gaurav Kashyap - IJLRP Volume 5, Issue 12, December 2024. DOI 10.5281/zenodo.14866681
DOI	https://doi.org/10.5281/zenodo.14866681
Short DOI	https://doi.org/g84xqg

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Unsupervised Learning for High-Dimensional Data: Advancements in Unsupervised Learning Techniques like Clustering, Anomaly Detection, and Dimensionality Reduction

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