Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Book Details
Format
Paperback / Softback
ISBN-10
103204103X
ISBN-13
9781032041032
Publisher
Taylor & Francis Ltd
Imprint
CRC Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Sep 25th, 2023
Print length
160 Pages
Weight
453 grams
Product Classification:
EconometricsEconometrics and economic statisticsEconometrics and economic statisticsEconomic statisticsProbability & statisticsProbability and statisticsElectrical engineeringAutomatic control engineeringDatabasesDatabases / Data managementData capture & analysisData capture and analysisData miningComputer scienceMathematical theory of computationMachine learning
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This book describes algorithms like Locally Linear Embedding, Laplacian eigenmaps, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in case of non-linear relationships within the data. Underlying mathematical concepts, derivations, proofs, strengths and limitations of these algorithms are discussed as well.
This book describes algorithms like Locally Linear Embedding, Laplacian eigenmaps, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in case of non-linear relationships within the data. Underlying mathematical concepts, derivations, proofs, strengths and limitations of these algorithms are discussed as well.
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