<--- Back to Details
First PageDocument Content
Linear combination / Projection / Basis / Dot product / Vector space / Euclidean vector / Comparison of vector algebra and geometric algebra / Euclidean subspace / Algebra / Mathematics / Linear algebra
Date: 2007-08-30 03:42:28
Linear combination
Projection
Basis
Dot product
Vector space
Euclidean vector
Comparison of vector algebra and geometric algebra
Euclidean subspace
Algebra
Mathematics
Linear algebra

[removed]The Basic Method Proof of Claim[removed]Suppose not. Then for some v, v 0 ∈ A we have u + v = u0 + v 0 , and hence, v + v 0 = u + u0 . Let c, c0 be the vectors from C for which

Add to Reading List

Source URL: lovelace.thi.informatik.uni-frankfurt.de

Download Document from Source Website

File Size: 83,90 KB

Share Document on Facebook

Similar Documents

Note to other teachers and users of these slides: We would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs.

Note to other teachers and users of these slides: We would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs.

DocID: 1rdhK - View Document

8  The Lanczos Method Erik Koch Computational Materials Science German Research School for Simulation Sciences

8 The Lanczos Method Erik Koch Computational Materials Science German Research School for Simulation Sciences

DocID: 1pIEI - View Document

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. X, NO. X, MONTH 20XX  1 Joint Speaker Verification and Anti-Spoofing in the i-Vector Space

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. X, NO. X, MONTH 20XX 1 Joint Speaker Verification and Anti-Spoofing in the i-Vector Space

DocID: 1ppWe - View Document

Contents 1 Singular Value Decomposition (SVD) 1.1 Singular Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Singular Value Decomposition (SVD) . . . . . . . . . . . . . . . . . 1.3 Best Rank k Approx

Contents 1 Singular Value Decomposition (SVD) 1.1 Singular Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Singular Value Decomposition (SVD) . . . . . . . . . . . . . . . . . 1.3 Best Rank k Approx

DocID: 1p8e8 - View Document

Odyssey 2012 The Speaker and Language Recognition WorkshopJune 2012, Singapore Speaker vectors from Subspace Gaussian Mixture Model as complementary features for Language Identification

Odyssey 2012 The Speaker and Language Recognition WorkshopJune 2012, Singapore Speaker vectors from Subspace Gaussian Mixture Model as complementary features for Language Identification

DocID: 1lnGC - View Document