{
"item_title" : "Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization",
"item_author" : [" B. K. Tripathy", "Anveshrithaa Sundareswaran", "Shrusti Ghela "],
"item_description" : "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.",
"item_img_path" : "https://covers3.booksamillion.com/covers/bam/1/03/204/101/1032041013_b.jpg",
"price_data" : {
"retail_price" : "249.99", "online_price" : "249.99", "our_price" : "249.99", "club_price" : "249.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : ""
}
}
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Other Available Formats
Overview
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 item is Non-Returnable
Customers Also Bought
Details
- ISBN-13: 9781032041018
- ISBN-10: 1032041013
- Publisher: CRC Press
- Publish Date: September 2021
- Dimensions: 9.21 x 6.14 x 0.44 inches
- Shipping Weight: 0.94 pounds
- Page Count: 160
Related Categories
