Get Free Shipping on orders over $89
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization - B.K. Tripathy

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

By: B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela

Hardcover | 2 September 2021 | Edition Number 1

At a Glance

Hardcover


RRP $368.00

$315.99

14%OFF

or 4 interest-free payments of $79.00 with

 or 

Available for Backorder. We will order this from our supplier however there isn't a current ETA.

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.

More in Computer Science

How to Talk to AI : (And How Not To) - Jamie Bartlett

RRP $26.99

$22.99

15%
OFF
Empire of AI : Inside the reckless race for total domination - Karen Hao
Microsoft 365 Excel For Dummies : For Dummies (Computer/Tech) - David H. Ringstrom
Microsoft 365 Excel All-in-One For Dummies : Excel for Dummies - David H. Ringstrom
Learning SOLIDWORKS 2026 : Modeling, Assembly and Analysis - Randy H. Shih
Psychiatry and Technology - Manoj  Shettar

RRP $373.95

$269.75

28%
OFF
AI and Maternal Health : Transformations and Implications - Jennifer Schindler-Ruwisch
AI and Maternal Health : Transformations and Implications - Jennifer Schindler-Ruwisch