LEADER 01142nam--2200373---450- 001 990003521490203316 005 20110413111607.0 035 $a000352149 035 $aUSA01000352149 035 $a(ALEPH)000352149USA01 035 $a000352149 100 $a20110413d1982----km-y0itay50------ba 101 $aita$alat 102 $aIT 105 $a||||||||001yy 200 1 $aM. Porci Catonis orationum reliquiae$gintroduzione, testo critico e commento filologico a cura di Maria Teresa Sblendorio Cugusi 210 $aTorino [etc.]$cParavia$d1982 215 $a550 p.$d22 cm 225 2 $aHistorica politica philosophica$v12 410 0$12001$aHistorica politica philosophica$v12 454 1$12001 461 1$1001-------$12001 606 0 $aLetteratura latina$2BNCF 676 $a878 700 1$aCATO,$bMarcus Porcius$071570 702 1$aSBLENDORIO CUGUSI,$bMaria Teresa 801 0$aIT$bsalbc$gISBD 912 $a990003521490203316 951 $aFL 8,2$b3421 DSA 959 $aBK 969 $aDSA 979 $aDSA$b90$c20110413$lUSA01$h1116 996 $aM. Porci Catonis Orationum reliquiae$9306229 997 $aUNISA LEADER 04857nam 2200613 450 001 9910830377103321 005 20240202030615.0 010 $a1-119-70098-1 010 $a1-119-70099-X 010 $a1-119-70096-5 024 7 $a10.1002/9781119700999 035 $a(CKB)4100000012000453 035 $a(MiAaPQ)EBC6707835 035 $a(Au-PeEL)EBL6707835 035 $a(OCoLC)1264474112 035 $a(CaSebORM)9781786301550 035 $a(OCoLC)1269508540 035 $a(OCoLC-P)1269508540 035 $a(EXLCZ)994100000012000453 100 $a20220510d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMatrix and tensor decompositions in signal processing$hVolume 2 /$fGerard Favier 210 1$aLondon, England :$cISTE Ltd,$d[2021] 210 4$d??2021 215 $a1 online resource (386 pages) 225 1 $aDigital signal and image processing series. Matrices and tensors in signal processing set. 311 $a1-78630-155-5 327 $aIntro -- Table of Contents -- Title Page -- Copyright -- Introduction -- I.1. What are the advantages of tensor approaches? -- I.2. For what uses? -- I.3. In what fields of application? -- I.4. With what tensor decompositions? -- I.5. With what cost functions and optimization algorithms? -- I.6. Brief description of content -- 1 Matrix Decompositions -- 1.1. Introduction -- 1.2. Overview of the most common matrix decompositions -- 1.3. Eigenvalue decomposition -- 1.4. URVH decomposition -- 1.5. Singular value decomposition -- 1.6. CUR decomposition -- 2 Hadamard, Kronecker and Khatri-Rao Products -- 2.1. Introduction -- 2.2. Notation -- 2.3. Hadamard product -- 2.4. Kronecker product -- 2.5. Kronecker sum -- 2.6. Index convention -- 2.7. Commutation matrices -- 2.8. Relations between the diag operator and the Kronecker product -- 2.9. Khatri-Rao product -- 2.10. Relations between vectorization and Kronecker and Khatri-Rao products -- 2.11. Relations between the Kronecker, Khatri-Rao and Hadamard products -- 2.12. Applications -- 3 Tensor Operations -- 3.1. Introduction -- 3.2. Notation and particular sets of tensors -- 3.3. Notion of slice -- 3.4. Mode combination -- 3.5. Partitioned tensors or block tensors -- 3.6. Diagonal tensors -- 3.7. Matricization -- 3.8. Subspaces associated with a tensor and multilinear rank -- 3.9. Vectorization -- 3.10. Transposition -- 3.11. Symmetric/partially symmetric tensors -- 3.12. Triangular tensors -- 3.13. Multiplication operations -- 3.14. Inverse and pseudo-inverse tensors -- 3.15. Tensor decompositions in the form of factorizations -- 3.16. Inner product, Frobenius norm and trace of a tensor -- 3.17. Tensor systems and homogeneous polynomials -- 3.18. Hadamard and Kronecker products of tensors -- 3.19. Tensor extension -- 3.20. Tensorization -- 3.21. Hankelization. 327 $a4 Eigenvalues and Singular Values of a Tensor -- 4.1. Introduction -- 4.2. Eigenvalues of a tensor of order greater than two -- 4.3. Best rank-one approximation -- 4.4. Orthogonal decompositions -- 4.5. Singular values of a tensor -- 5 Tensor Decompositions -- 5.1. Introduction -- 5.2. Tensor models -- 5.3. Examples of tensor models -- Appendix Random Variables and Stochastic Processes -- A1.1. Introduction -- A1.2. Random variables -- A1.3. Discrete-time random signals -- A1.4. Application to system identification -- References -- Index -- End User License Agreement. 330 $aThe second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief historical review of the compressed sampling methods, an overview of the main methods of retrieving matrices and tensors with missing data will be performed under the low rank hypothesis. Illustrative examples will be provided. 410 0$aDigital signal and image processing series. 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