Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Tripathy, B. K., Sundareswaran, Anveshrithaa, Ghela, Shrusti
- 出版商: CRC
- 出版日期: 2023-09-25
- 售價: $2,830
- 貴賓價: 9.5 折 $2,689
- 語言: 英文
- 頁數: 160
- 裝訂: Quality Paper - also called trade paper
- ISBN: 103204103X
- ISBN-13: 9781032041032
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相關分類:
Data-visualization
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相關主題
商品描述
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and 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 limitations. The book highlights important use cases of these algorithms and provides 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 a 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 is aimed 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.
商品描述(中文翻譯)
「無監督學習方法用於降維和數據可視化」描述了一些算法,如局部線性嵌入(LLE)、拉普拉斯特徵圖、Isomap、半定嵌入和t-SNE,用於解決數據中非線性關係的降維問題。本書討論了這些算法的基礎數學概念、推導和證明,並提供了邏輯解釋,包括優點和局限性。書中突出了這些算法的重要應用案例,並提供了示例和可視化。還介紹了這些算法的比較研究,以便選擇最適合的算法來實現高效的降維和數據可視化。
特點:
- 示範了如何使用無監督學習方法進行降維
- 清晰解釋算法,重點放在基礎和基本的數學概念上
- 描述了算法的比較研究,並討論了每個算法在何時何地最適合使用
- 提供了每個算法的應用案例、示例和可視化
- 幫助可視化和創建高維和複雜數據的緊湊表示,適用於各種實際應用和數據分析
本書針對計算機科學和工程、數據科學、機器學習、計算機視覺、數據挖掘、深度學習、傳感器數據過濾、控制系統特徵提取和醫療儀器輸入提取等專業人士、研究生和研究人員。
作者簡介
作者簡介(中文翻譯)
B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela