Data Representation, Preprocessing, and Dimensionality Reduction

Parveen Kumari

ISBN 10: 6630237846 / ISBN 13: 9786630237849

Published by LAP LAMBERT Academic Publishing Jul 2026, 2026

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Data Representation, Preprocessing, and Dimensionality Reduction provides a comprehensive introduction to the essential techniques used for preparing data for analytics, machine learning, and artificial intelligence applications. The book explores methods for representing structured and unstructured data, handling missing values, noise, and inconsistencies, and transforming raw data into meaningful formats. It also covers feature selection, feature extraction, normalization, encoding, and dimensionality reduction techniques such as PCA and other modern approaches. Through practical examples and real-world applications, the book demonstrates how effective data preprocessing and dimensionality reduction improve model performance, computational efficiency, and decision-making accuracy in data-driven environments.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 244 pp. Englisch.

Seller Inventory # 9786630237849

Bibliographic Details

• Title: Data Representation, Preprocessing, and Dimensionality Reduction
• Publisher: LAP LAMBERT Academic Publishing Jul 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 6630237846
• ISBN 13: 9786630237849
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 15D millimeters 220 millimeters width by 150 millimeters height by 15 millimeters depth
• Weight: 381 grams

About this title

Synopsis

Data Representation, Preprocessing, and Dimensionality Reduction provides a comprehensive introduction to the essential techniques used for preparing data for analytics, machine learning, and artificial intelligence applications. The book explores methods for representing structured and unstructured data, handling missing values, noise, and inconsistencies, and transforming raw data into meaningful formats. It also covers feature selection, feature extraction, normalization, encoding, and dimensionality reduction techniques such as PCA and other modern approaches. Through practical examples and real-world applications, the book demonstrates how effective data preprocessing and dimensionality reduction improve model performance, computational efficiency, and decision-making accuracy in data-driven environments.