LEADER 04254nam  22006615  450 
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010   $a981-10-6704-X
024 7 $a10.1007/978-981-10-6704-4
035   $a(CKB)4100000001381542
035   $a(DE-He213)978-981-10-6704-4
035   $a(MiAaPQ)EBC5191634
035   $a(PPN)222226560
035   $a(EXLCZ)994100000001381542
100   $a20171213d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAdvances in Principal Component Analysis $eResearch and Development /$fedited by Ganesh R. Naik
205   $a1st ed. 2018.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2018.
215   $a1 online resource (VII, 252 p. 94 illus., 75 illus. in color.) 
311   $a981-10-6703-1 
320   $aIncludes bibliographical references at the end of each chapters.
327   $aTheory -- Basic principles of PCA -- Geometric Principles of PCA -- Principal components and Correlation -- PCA in Regression analysis matrices -- PCA in cluster analysis -- PCA and factor analysis -- PCA for time series and independent data (ICA) -- Sparse PCA -- Non-negative PCA -- Applications of PCA -- PCA for Electrocardiography (ECG) applications -- PCA for Electroencephalography (EEG) applications -- PCA for Electromyography (EMG) applications -- PCA for bioinformatics and gene expression applications -- PCA for human movement science applications -- PCA for Gait Kinematics for Patients with Knee Osteoarthritis -- Neuroscience and biomedical application of PCA -- PCA applications for Brain Computer Interface (BCI) and motor imagery tasks -- PCA for Image processing applications -- PCA for Video processing applications -- PCA for dimensional reduction applications -- PCA for financial and economics applications.
330   $aThis book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail. Bringing together research results previously scattered throughout many scientific journals papers worldwide, the book presents them in a methodologically unified form. Offering vital insights into the subject matter in self-contained chapters that balance the theory and concrete applications, and especially focusing on open problems, it is essential reading for all researchers and practitioners with an interest in PCA.
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aPattern perception
606   $aComputational intelligence
606   $aComputer science$xMathematics
606   $aBiomedical engineering
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X
606   $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615  0$aPattern perception.
615  0$aComputational intelligence.
615  0$aComputer science$xMathematics.
615  0$aBiomedical engineering.
615 14$aSignal, Image and Speech Processing.
615 24$aPattern Recognition.
615 24$aComputational Intelligence.
615 24$aComputational Mathematics and Numerical Analysis.
615 24$aBiomedical Engineering and Bioengineering.
676   $a621.382
702   $aNaik$b Ganesh R$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910299582303321
996   $aAdvances in Principal Component Analysis$92504341
997   $aUNINA