04990nam 22008175 450 99641817470331620200705095718.03-030-40794-210.1007/978-3-030-40794-0(CKB)4100000010858861(DE-He213)978-3-030-40794-0(MiAaPQ)EBC6157459(PPN)243762763(EXLCZ)99410000001085886120200403d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierFeature Learning and Understanding[electronic resource] Algorithms and Applications /by Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (XIV, 291 p. 126 illus., 109 illus. in color.) Information Fusion and Data Science,2510-15283-030-40793-4 Chapter1. A Gentle Introduction to Feature Learning -- Chapter2. Latent Semantic Feature Learning -- Chapter3. Principal Component Analysis -- Chapter4. Local-Geometrical-Structure-based Feature Learning -- Chapter5. Linear Discriminant Analysis -- Chapter6. Kernel-based nonlinear feature learning -- Chapter7. Sparse feature learning -- Chapter8. Low rank feature learning -- Chapter9. Tensor-based Feature Learning -- Chapter10. Neural-network-based Feature Learning: Autoencoder -- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network -- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network.This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.Information Fusion and Data Science,2510-1528SociophysicsEconophysicsMachine learningComputational intelligencePattern recognitionSignal processingImage processingSpeech processing systemsOptical data processingData-driven Science, Modeling and Theory Buildinghttps://scigraph.springernature.com/ontologies/product-market-codes/P33030Machine Learninghttps://scigraph.springernature.com/ontologies/product-market-codes/I21010Computational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XSignal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Sociophysics.Econophysics.Machine learning.Computational intelligence.Pattern recognition.Signal processing.Image processing.Speech processing systems.Optical data processing.Data-driven Science, Modeling and Theory Building.Machine Learning.Computational Intelligence.Pattern Recognition.Signal, Image and Speech Processing.Image Processing and Computer Vision.006.31Zhao Haitaoauthttp://id.loc.gov/vocabulary/relators/aut935380Lai Zhihuiauthttp://id.loc.gov/vocabulary/relators/autLeung Henryauthttp://id.loc.gov/vocabulary/relators/autZhang Xianyiauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK996418174703316Feature Learning and Understanding2106932UNISA