Latent Factor Analysis for High-Dimensional and Sparse Matrices
Home > References & Encyclopaedias > Research & information: general > Information theory > Latent Factor Analysis for High-Dimensional and Sparse Matrices
Latent Factor Analysis for High-Dimensional and Sparse Matrices

Latent Factor Analysis for High-Dimensional and Sparse Matrices


     0     
5
4
3
2
1



International Edition


About the Book

Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question.

This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications.

The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.


About the Author: Dr. Ye Yuan is an Associate Professor at the College of Computer and Information Science, Southwest University. His main research fields are data mining and machine learning. He has published over 24 SCI/EI papers, including for top journals and conferences like IEEE T. KDE, CYB, WWW and ECAI. He has applied for 11 and holds 5 national invention patents and won First Prize in the Wu Wenjun AI Science and Technology Progress Award and First Prize in the Chongqing Science and Technology Progress Award.

Dr. Xin Luo is a Professor at the College of Computer and Information Science, Southwest University. His current research interests include machine intelligence, big data, and cloud computing. He has published over 200 papers (including over 87 IEEE TRANSACTIONS papers and 17 highly cited papers in ESI) in the above areas. He holds 35 national invention patents. He was part of the Pioneer Hundred Talents Program of the Chinese Academy of Sciences in 2016, the Advanced Support of the Pioneer Hundred Talents Program of Chinese Academy of Sciences in 2018, and the National High-Level Talents Special Support Program in 2020. He won First Prize in the Chongqing Natural Science Award (2019), First Prize in the Wu Wenjun AI Science and Technology Progress Award (2018) and First Prize in the Chongqing Science and Technology Progress Award (2018). He serves as an Associate Editor for the IEEE/CAA Journal of Automatica Sinica, and for IEEE Transactions on Neural Networks and Learning Systems. He received the Outstanding Associate Editor Award from the IEEE/CAA Journal of Automatica Sinica in 2020.


Best Sellers



Product Details
  • ISBN-13: 9789811967023
  • Publisher: Springer Nature Singapore
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: A Particle Swarm Optimization-Based Approach
  • Width: 156 mm
  • ISBN-10: 9811967024
  • Publisher Date: 16 Nov 2022
  • Height: 234 mm
  • No of Pages: 92
  • Spine Width: 5 mm
  • Weight: 209 gr


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Latent Factor Analysis for High-Dimensional and Sparse Matrices
Springer Nature Singapore -
Latent Factor Analysis for High-Dimensional and Sparse Matrices
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Latent Factor Analysis for High-Dimensional and Sparse Matrices

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals



    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!