Tracked shipping to South Africa with premium packaging for just R199 

Ship to
South Africa
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Type
Physical Book
Publisher
Author
Language
English
Pages
174
Format
Paperback
ISBN13
9781032041032
Edition No.
1

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

B. K. Tripathy Anveshrithaa S. Shrusti Ghela (Author) · Crc Press · Paperback

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization - B. K. Tripathy Anveshrithaa S. Shrusti Ghela

Cheaper New Book Imported to South Africa
Delivery: 10 Sep - 23 Sep Shipping: 18 to 22 business days.
R 1,375
Faster New Book Imported to South Africa
Delivery: 20 Aug - 28 Aug Shipping: 4 to 5 business days.
R 2,076
R 1,375

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization "

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews