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The transformation P is the orthogonal projection onto the line m. In linear algebra and functional analysis , a projection is a linear transformation P {\displaystyle P} from a vector space to itself (an endomorphism ) such that P ∘ P = P {\displaystyle P\circ P=P} .
The vector projection (also known as the vector component or vector resolution) of a vector a on (or onto) a nonzero vector b is the orthogonal projection of a onto a straight line parallel to b. The projection of a onto b is often written as proj b a {\displaystyle \operatorname {proj} _{\mathbf {b} }\mathbf {a} } or a ∥ b .
Orthographic projection (also orthogonal projection and analemma) [a] is a means of representing three-dimensional objects in two dimensions.Orthographic projection is a form of parallel projection in which all the projection lines are orthogonal to the projection plane, [2] resulting in every plane of the scene appearing in affine transformation on the viewing surface.
The vector displacement from x to y is nonzero because the points are distinct, and represents the direction of the line. That is, every displacement between points on the line L is a scalar multiple of d = y – x. If a physical particle of unit mass were to move from x to y, it would have a moment about the origin of
For linear models, the trace of the projection matrix is equal to the rank of , which is the number of independent parameters of the linear model. [8] For other models such as LOESS that are still linear in the observations y {\displaystyle \mathbf {y} } , the projection matrix can be used to define the effective degrees of freedom of the model.
The linear least squares problem is to find the x that minimizes ‖ Ax − b ‖, which is equivalent to projecting b to the subspace spanned by the columns of A. Assuming the columns of A (and hence R) are independent, the projection solution is found from A T Ax = A T b. Now A T A is square (n × n) and invertible, and also equal to R T R.
For example, the mapping that takes a point (x, y, z) in three dimensions to the point (x, y, 0) is a projection. This type of projection naturally generalizes to any number of dimensions n for the domain and k ≤ n for the codomain of the mapping. See Orthogonal projection, Projection (linear algebra). In the case of orthogonal projections ...
is exactly a sought for orthogonal projection of onto an image of X (see the picture below and note that as explained in the next section the image of X is just a subspace generated by column vectors of X). A few popular ways to find such a matrix S are described below.