Projection Matrix: Difference between revisions
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A projection matrix <math>P</math> is a <math>n \times n</math> square matrix that gives a vector space [[projection]] from the [[n-dimensional space]] <math>\R^n</math> to a subspace <math>W</math>. | A projection matrix <math>P</math> is a <math>n \times n</math> square matrix that gives a [[vector space]] [[projection]] from the [[n-dimensional space]] <math>\R^n</math> to a [[subspace]] <math>W</math>. | ||
* <B>Context:</B> | * <B>Context:</B> | ||
** It can also be stated as: A square matrix <math>P</math> is a projection matrix if and only if <math>P^2=P</math> | ** It can also be stated as: A square matrix <math>P</math> is a projection matrix if and only if <math>P^2=P</math> | ||
** | ** It can be a symmetric matrix if and only if the vector space projection is [[orthogonal]]. | ||
** It can be a [[Hermitian matrix]] if and only if the vector space projection satisfies <math><v,P_w>=<v_W,P_w>=<P_v,w></math>, where the inner product is the [[Hermitian inner product]]. Here <math>P_w \in W and V_w \in W</math> | |||
* <B>Example(s):</B> | * <B>Example(s):</B> | ||
** An example of a nonsymmetric projection matrix is <math>\begin{bmatrix}0 & 1 \\ 0 & 1\end{bmatrix}</math>, which projects onto the line <math>y=x</math>. | ** An example of a [[nonsymmetric]] projection matrix is <math>\begin{bmatrix}0 & 1 \\ 0 & 1\end{bmatrix}</math>, which projects onto the line <math>y=x</math>. | ||
* <B>See:</B> [[Linear Transformation]]. | * <B>See:</B> [[Linear Transformation]]. | ||
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Revision as of 13:16, 14 January 2016
A projection matrix [math]\displaystyle{ P }[/math] is a [math]\displaystyle{ n \times n }[/math] square matrix that gives a vector space projection from the n-dimensional space [math]\displaystyle{ \R^n }[/math] to a subspace [math]\displaystyle{ W }[/math].
- Context:
- It can also be stated as: A square matrix [math]\displaystyle{ P }[/math] is a projection matrix if and only if [math]\displaystyle{ P^2=P }[/math]
- It can be a symmetric matrix if and only if the vector space projection is orthogonal.
- It can be a Hermitian matrix if and only if the vector space projection satisfies [math]\displaystyle{ \lt v,P_w\gt =\lt v_W,P_w\gt =\lt P_v,w\gt }[/math], where the inner product is the Hermitian inner product. Here [math]\displaystyle{ P_w \in W and V_w \in W }[/math]
- Example(s):
- An example of a nonsymmetric projection matrix is [math]\displaystyle{ \begin{bmatrix}0 & 1 \\ 0 & 1\end{bmatrix} }[/math], which projects onto the line [math]\displaystyle{ y=x }[/math].
- See: Linear Transformation.
References
2009
- http://en.wikipedia.org/wiki/Projection_(linear_algebra)
- In linear algebra and functional analysis, a projection is a linear transformation [math]\displaystyle{ P }[/math] from a vector space to itself such that P2 = P. It leaves its image unchanged. Though abstract, this definition of "projection" formalizes and generalizes the idea of graphical projection. One can also consider the effect of a projection on a geometrical object by examining the effect of the projection on points in the object.