Unsupervised learning approaches for dimensionality reduction and data visualization / B.K. Tripathy, Anveshrithaa S, Shrusti Ghela.
By: Tripathy, B. K [author.]
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Contributor(s): S., Anveshrithaa [author.] | Ghela, Shrusti [author.].
Material type: 
BookPublisher: Boca Raton : CRC Press Book, 2022Edition: First edition.Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781003190554; 1003190553; 9781000438451; 1000438457; 9781000438314; 1000438317.Subject(s): Information visualization | Data reduction | Machine learning | BUSINESS & ECONOMICS / Statistics | COMPUTERS / Database Management / Data Mining | COMPUTERS / Machine TheoryDDC classification: 001.4/226 Online resources: Taylor & Francis | OCLC metadata license agreement Summary: "This book describes algorithms like Locally Linear Embedding (LLE), Laplacian eigenmaps, Isomap, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed including strengths and the limitations. It highlights important use cases of these algorithms and few examples along with visualizations. Comparative study of the algorithms is presented, to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. Features: Demonstrates how unsupervised learning approaches can be used for dimensionality reduction. Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts. Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use. Provides use cases, illustrative examples, and visualizations of each algorithm. Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis. This book aims at professionals, graduate students and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction"-- Provided by publisher.
"This book describes algorithms like Locally Linear Embedding (LLE), Laplacian eigenmaps, Isomap, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed including strengths and the limitations. It highlights important use cases of these algorithms and few examples along with visualizations. Comparative study of the algorithms is presented, to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. Features: Demonstrates how unsupervised learning approaches can be used for dimensionality reduction. Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts. Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use. Provides use cases, illustrative examples, and visualizations of each algorithm. Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis. This book aims at professionals, graduate students and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction"-- Provided by publisher.
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