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<math>x, y, z \in X</math>
 
<math>x, y, z \in X</math>
  
1. <math>d(x,y)=d(y,x)</math>
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#<math>d(x,y)=d(y,x)</math>
 
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#<math>d(x,z)\leq d(x,y)+d(y,z)</math>
2. <math>d(x,z)\leq d(x,y)+d(y,z)</math>
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#<math>d(x,y) \geq 0, d(x,y)=0 \Leftrightarrow x=y)</math>
 
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3. <math>d(x,y) \geq 0, d(x,y)=0 \Leftrightarrow x=y)</math>
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If X is vector space, metric can be induced by the norm <math>||\cdot||</math>.
 
If X is vector space, metric can be induced by the norm <math>||\cdot||</math>.
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<math>||\cdot||: X \rightarrow \Re ^{+}</math>
 
<math>||\cdot||: X \rightarrow \Re ^{+}</math>
  
1. <math>|x| \geq 0, ||x||=0 \Leftrightarrow x=0</math>
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#<math>|x| \geq 0, ||x||=0 \Leftrightarrow x=0</math>
2. <math>||\alpha x||=|\alpha | ||x||</math>
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#<math>||\alpha x||=|\alpha | ||x||</math>
3. <math>||x+y|| \leq ||x|| + || ||</math>
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#<math>||x+y|| \leq ||x|| + ||y||</math>
  
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[[Category:ECE662]]
 
Defining metric, we can measure similarity of elements of set X.
 
Defining metric, we can measure similarity of elements of set X.
  
 
Example of metric
 
Example of metric
1. Minkowski Metric <math> \left( \sum_{i=1}^n \left| x_i - y_i \right|^p \right)^{1/p}</math>
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#Minkowski Metric <math> \left( \sum_{i=1}^n \left| x_i - y_i \right|^p \right)^{1/p}</math>
 
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#Riemannian Metric <math>D(\vec{x_1},\vec{x_2})=\sqrt{(\vec{x_1}-\vec{x_2})^\top \mathbb{M}(\vec{x_1}-\vec{x_2})}</math>
2. Riemannian Metric <math>D(\vec{x_1},\vec{x_2})=\sqrt{(\vec{x_1}-\vec{x_2})^\top \mathbb{M}(\vec{x_1}-\vec{x_2})}</math>
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#Tanimoto metric <math>D(S_1, S_2) = \frac {|S_1|+|S_2|-2|S_1 \bigcap S_2| }{|S_1|+|S_2|-|S_1 \bigcap S_2|} </math>
 
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#Procrustes metric <math>D(p,\bar p)= min_{R,T} \sum_{i=1}^n
3. Tanimoto metric <math>D(S_1, S_2) = \frac {|S_1|+|S_2|-2|S_1 \bigcap S_2| }{|S_1|+|S_2|-|S_1 \bigcap S_2|} </math>
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{\begin{Vmatrix} Rp_i+T-\bar p_i \end{Vmatrix}} _{L^2} </math>, R: Rotation, T: Translation
 
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4. Procrustes metric <math>D(p,\bar p)= \sum_{\begin{matrix}i=1 \\ rotation R, translation T \end{matrix}}^n
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{\begin{Vmatrix} Rp_i+T-\bar p_i \end{Vmatrix}} _{L^2} </math>
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Latest revision as of 09:34, 10 April 2008

Metric Space (X,d) $ d:X \times X \rightarrow \Re ^{+} $

X is set, not necessarily vector space

$ x, y, z \in X $

  1. $ d(x,y)=d(y,x) $
  2. $ d(x,z)\leq d(x,y)+d(y,z) $
  3. $ d(x,y) \geq 0, d(x,y)=0 \Leftrightarrow x=y) $

If X is vector space, metric can be induced by the norm $ ||\cdot|| $.

$ d(x,y)=||y-x|| $

Norm is defined as follows

$ ||\cdot||: X \rightarrow \Re ^{+} $

  1. $ |x| \geq 0, ||x||=0 \Leftrightarrow x=0 $
  2. $ ||\alpha x||=|\alpha | ||x|| $
  3. $ ||x+y|| \leq ||x|| + ||y|| $

Defining metric, we can measure similarity of elements of set X.

Example of metric

  1. Minkowski Metric $ \left( \sum_{i=1}^n \left| x_i - y_i \right|^p \right)^{1/p} $
  2. Riemannian Metric $ D(\vec{x_1},\vec{x_2})=\sqrt{(\vec{x_1}-\vec{x_2})^\top \mathbb{M}(\vec{x_1}-\vec{x_2})} $
  3. Tanimoto metric $ D(S_1, S_2) = \frac {|S_1|+|S_2|-2|S_1 \bigcap S_2| }{|S_1|+|S_2|-|S_1 \bigcap S_2|} $
  4. Procrustes metric $ D(p,\bar p)= min_{R,T} \sum_{i=1}^n {\begin{Vmatrix} Rp_i+T-\bar p_i \end{Vmatrix}} _{L^2} $, R: Rotation, T: Translation

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva