The definition of Affine Invariant Riemannian Metric (AIRM)












1














For any two symmetric positive definite (SPD) matrices $A$ and $B$, the Affine Invariant Riemannian Metric (AIRM) between them is defined as [1], [2]:



$d(A,B)=||log (A^{-1/2}B A^{-1/2}) ||_F$,



where $log(A)$ is the matrix logrithm of $A$, and $||A||_F$ is the Frobenius norm of $A$.



On the other hand, it is also shown that (e.g., see [3])



$d(A,B)=||log (A^{-1/2}B A^{-1/2}) ||_F = ||log (A^{-1}B) ||_F$.



However, when I used Matlab function logm, I found that $||log (A^{-1/2}B A^{-1/2}) ||_F ne ||log (A^{-1}B) ||_F$ at all (but their eigenvalues are the same).



I was wondering if there is anything wrong with my understanding the definition of AIRM?



Thanks very much!



[1] R. Bhatia, Positive Definite Matrices. Princeton University Press, 2009.



[2] X. Pennec, P. Fillard, and N. Ayache, “A Riemannian framework for tensor computing,” International Journal of Computer Vision, vol. 66, no. 1, pp. 41–66, 2006.



[3] M. Moakher, “A differential geometric approach to the geometric mean of symmetric Positive-Definite matrices,” SIAM J. Matrix Anal. Appl., vol. 26, no. 3, pp. 735–747, 2005.



[4] I. Horev, F. Yger, and M. Sugiyama, “Geometry-aware principal component analysis for symmetric positive definite matrices,” Machine Learning, 2017.










share|cite|improve this question



























    1














    For any two symmetric positive definite (SPD) matrices $A$ and $B$, the Affine Invariant Riemannian Metric (AIRM) between them is defined as [1], [2]:



    $d(A,B)=||log (A^{-1/2}B A^{-1/2}) ||_F$,



    where $log(A)$ is the matrix logrithm of $A$, and $||A||_F$ is the Frobenius norm of $A$.



    On the other hand, it is also shown that (e.g., see [3])



    $d(A,B)=||log (A^{-1/2}B A^{-1/2}) ||_F = ||log (A^{-1}B) ||_F$.



    However, when I used Matlab function logm, I found that $||log (A^{-1/2}B A^{-1/2}) ||_F ne ||log (A^{-1}B) ||_F$ at all (but their eigenvalues are the same).



    I was wondering if there is anything wrong with my understanding the definition of AIRM?



    Thanks very much!



    [1] R. Bhatia, Positive Definite Matrices. Princeton University Press, 2009.



    [2] X. Pennec, P. Fillard, and N. Ayache, “A Riemannian framework for tensor computing,” International Journal of Computer Vision, vol. 66, no. 1, pp. 41–66, 2006.



    [3] M. Moakher, “A differential geometric approach to the geometric mean of symmetric Positive-Definite matrices,” SIAM J. Matrix Anal. Appl., vol. 26, no. 3, pp. 735–747, 2005.



    [4] I. Horev, F. Yger, and M. Sugiyama, “Geometry-aware principal component analysis for symmetric positive definite matrices,” Machine Learning, 2017.










    share|cite|improve this question

























      1












      1








      1







      For any two symmetric positive definite (SPD) matrices $A$ and $B$, the Affine Invariant Riemannian Metric (AIRM) between them is defined as [1], [2]:



      $d(A,B)=||log (A^{-1/2}B A^{-1/2}) ||_F$,



      where $log(A)$ is the matrix logrithm of $A$, and $||A||_F$ is the Frobenius norm of $A$.



      On the other hand, it is also shown that (e.g., see [3])



      $d(A,B)=||log (A^{-1/2}B A^{-1/2}) ||_F = ||log (A^{-1}B) ||_F$.



      However, when I used Matlab function logm, I found that $||log (A^{-1/2}B A^{-1/2}) ||_F ne ||log (A^{-1}B) ||_F$ at all (but their eigenvalues are the same).



      I was wondering if there is anything wrong with my understanding the definition of AIRM?



      Thanks very much!



      [1] R. Bhatia, Positive Definite Matrices. Princeton University Press, 2009.



      [2] X. Pennec, P. Fillard, and N. Ayache, “A Riemannian framework for tensor computing,” International Journal of Computer Vision, vol. 66, no. 1, pp. 41–66, 2006.



      [3] M. Moakher, “A differential geometric approach to the geometric mean of symmetric Positive-Definite matrices,” SIAM J. Matrix Anal. Appl., vol. 26, no. 3, pp. 735–747, 2005.



      [4] I. Horev, F. Yger, and M. Sugiyama, “Geometry-aware principal component analysis for symmetric positive definite matrices,” Machine Learning, 2017.










      share|cite|improve this question













      For any two symmetric positive definite (SPD) matrices $A$ and $B$, the Affine Invariant Riemannian Metric (AIRM) between them is defined as [1], [2]:



      $d(A,B)=||log (A^{-1/2}B A^{-1/2}) ||_F$,



      where $log(A)$ is the matrix logrithm of $A$, and $||A||_F$ is the Frobenius norm of $A$.



      On the other hand, it is also shown that (e.g., see [3])



      $d(A,B)=||log (A^{-1/2}B A^{-1/2}) ||_F = ||log (A^{-1}B) ||_F$.



      However, when I used Matlab function logm, I found that $||log (A^{-1/2}B A^{-1/2}) ||_F ne ||log (A^{-1}B) ||_F$ at all (but their eigenvalues are the same).



      I was wondering if there is anything wrong with my understanding the definition of AIRM?



      Thanks very much!



      [1] R. Bhatia, Positive Definite Matrices. Princeton University Press, 2009.



      [2] X. Pennec, P. Fillard, and N. Ayache, “A Riemannian framework for tensor computing,” International Journal of Computer Vision, vol. 66, no. 1, pp. 41–66, 2006.



      [3] M. Moakher, “A differential geometric approach to the geometric mean of symmetric Positive-Definite matrices,” SIAM J. Matrix Anal. Appl., vol. 26, no. 3, pp. 735–747, 2005.



      [4] I. Horev, F. Yger, and M. Sugiyama, “Geometry-aware principal component analysis for symmetric positive definite matrices,” Machine Learning, 2017.







      matrices riemannian-geometry






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      asked Nov 16 '18 at 7:10









      user3138073

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          The reason for this is that $log(A^{-1}B)$ isn't even a symmetric matrix. Notice that while $A$ and $B$ are symmetric, $A^{-1}B$ isn't necessarily so, even though $A^{-1/2}BA^{-1/2}$ is symmetric positive-definite. The logarithm likely obscures the way that Matlab computes the Frobenius norm of a matrix.






          share|cite|improve this answer





















          • Thanks for the feedback! I agree with you that $A^{-1}B$ is not symmetric, but still, in [3], the author used $||log(A^{-1}B)||_F$ as AIRM, which is confusing to me.
            – user3138073
            Dec 27 '18 at 3:39











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          The reason for this is that $log(A^{-1}B)$ isn't even a symmetric matrix. Notice that while $A$ and $B$ are symmetric, $A^{-1}B$ isn't necessarily so, even though $A^{-1/2}BA^{-1/2}$ is symmetric positive-definite. The logarithm likely obscures the way that Matlab computes the Frobenius norm of a matrix.






          share|cite|improve this answer





















          • Thanks for the feedback! I agree with you that $A^{-1}B$ is not symmetric, but still, in [3], the author used $||log(A^{-1}B)||_F$ as AIRM, which is confusing to me.
            – user3138073
            Dec 27 '18 at 3:39
















          0














          The reason for this is that $log(A^{-1}B)$ isn't even a symmetric matrix. Notice that while $A$ and $B$ are symmetric, $A^{-1}B$ isn't necessarily so, even though $A^{-1/2}BA^{-1/2}$ is symmetric positive-definite. The logarithm likely obscures the way that Matlab computes the Frobenius norm of a matrix.






          share|cite|improve this answer





















          • Thanks for the feedback! I agree with you that $A^{-1}B$ is not symmetric, but still, in [3], the author used $||log(A^{-1}B)||_F$ as AIRM, which is confusing to me.
            – user3138073
            Dec 27 '18 at 3:39














          0












          0








          0






          The reason for this is that $log(A^{-1}B)$ isn't even a symmetric matrix. Notice that while $A$ and $B$ are symmetric, $A^{-1}B$ isn't necessarily so, even though $A^{-1/2}BA^{-1/2}$ is symmetric positive-definite. The logarithm likely obscures the way that Matlab computes the Frobenius norm of a matrix.






          share|cite|improve this answer












          The reason for this is that $log(A^{-1}B)$ isn't even a symmetric matrix. Notice that while $A$ and $B$ are symmetric, $A^{-1}B$ isn't necessarily so, even though $A^{-1/2}BA^{-1/2}$ is symmetric positive-definite. The logarithm likely obscures the way that Matlab computes the Frobenius norm of a matrix.







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered Dec 26 '18 at 23:24









          Mnifldz

          6,81311534




          6,81311534












          • Thanks for the feedback! I agree with you that $A^{-1}B$ is not symmetric, but still, in [3], the author used $||log(A^{-1}B)||_F$ as AIRM, which is confusing to me.
            – user3138073
            Dec 27 '18 at 3:39


















          • Thanks for the feedback! I agree with you that $A^{-1}B$ is not symmetric, but still, in [3], the author used $||log(A^{-1}B)||_F$ as AIRM, which is confusing to me.
            – user3138073
            Dec 27 '18 at 3:39
















          Thanks for the feedback! I agree with you that $A^{-1}B$ is not symmetric, but still, in [3], the author used $||log(A^{-1}B)||_F$ as AIRM, which is confusing to me.
          – user3138073
          Dec 27 '18 at 3:39




          Thanks for the feedback! I agree with you that $A^{-1}B$ is not symmetric, but still, in [3], the author used $||log(A^{-1}B)||_F$ as AIRM, which is confusing to me.
          – user3138073
          Dec 27 '18 at 3:39


















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