MARC details
| 000 -LEADER |
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02567nam a2200301Ia 4500 |
| 000 - LEADER |
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03305nam a22003255i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
978-981-19-8140-1 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240319120752.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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221206s2023 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789811981401 |
| -- |
978-981-19-8140-1 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
5.7 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Wu, Di. |
| 9 (RLIN) |
28186 |
| 245 ## - TITLE STATEMENT |
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| Title |
Robust Latent Feature Learning for Incomplete Big Data |
| Statement of responsibility, etc. |
by Di Wu. |
| Medium |
[electronic resource] / |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
1st ed. 2023. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Singapore |
| Name of publisher, distributor, etc. |
Springer Nature Singapore |
| Date of publication, distribution, etc. |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XIII, 112 p. 1 illus. |
| Other physical details |
online resource. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Artificial intelligence |
| 9 (RLIN) |
28187 |
|
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| Topical term or geographic name entry element |
Data Analysis and Big Data. |
| 9 (RLIN) |
28188 |
|
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| Topical term or geographic name entry element |
Data Mining and Knowledge Discovery. |
| 9 (RLIN) |
28189 |
|
|---|
| Topical term or geographic name entry element |
Data mining. |
| 9 (RLIN) |
28190 |
|
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| Topical term or geographic name entry element |
Data Science. |
| 9 (RLIN) |
28191 |
|
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| Topical term or geographic name entry element |
Quantitative research. |
| 9 (RLIN) |
28192 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://doi.org/10.1007/978-981-19-8140-1">https://doi.org/10.1007/978-981-19-8140-1</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
e-Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
