Question about projections and subspaces

In summary, to calculate the projection of a single vector v onto a subspace W, we use the formula <v, w1>w1 + <v, w2>w2 + ... <v, wn>wn, where <v, wi> represents the inner product between v and each basis vector in W. However, if the individual values of v are unknown, the matrix of projection can be calculated using the formula A(A^{T}A)^{-1}A^{T}, where A is the matrix formed from the basis vectors in W. This is necessary because projection is a linear operator and requires a matrix to operate on.
  • #1
JamesGoh
143
0
If we want to caculate the projection of a single vector, v=(1,2) (which is an element of an R2 vector space called V) onto the subspace of V (which we call W), do we use

projection of v onto W = <v,w1>w1 + <v,w2>w2 + ... <v,wn>wn

However, if the individual values of v are not known (that is v=(x,y) ), do we calculate the matrix of projection ?

that is, we do A(A[itex]^{T}[/itex]A)[itex]^{-1}[/itex]A[itex]^{T}[/itex]

If we have to determine the matrix of projection, is it because we don't know what x and y is, so the safe bet is to determine the matrix of projection ?
 
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  • #2
I really have no idea exactly what are asking. If you have a subspace, W, of an inner product space, V, and [itex]\{w_1, w2, ..., v_n\}[/itex] is an orthonormal basis for W, then, yes, the projection of v, a vector in v, onto W is [itex]<v, w_1>w_1+ <v, w_2>w_2+ ...+ <v, w_n>wn[/itex]. If you v is a "general" vector, then, yes, there exist a matrix such that multiplying any vector v by that matrix giives the projection. That is obviously true because projection is a linear operator. But I have no idea what you mean by "[itex]A(A^TA)^{-1}A^T[/itex]" because I do not know what "A" is. Where did you get the matrix A?
 
  • #3
sorry A is the matrix that is formed from the basis vectors in subspace W

where W = { basis 1, basis 2 }
 

Related to Question about projections and subspaces

1. What is a projection in linear algebra?

A projection in linear algebra is a transformation that maps a vector onto a subspace. It essentially "projects" the vector onto the subspace, creating a new vector that lies within the subspace.

2. How is a projection represented mathematically?

A projection onto a subspace can be represented by a projection matrix, which is an n x n matrix where n is the dimension of the vector space. It has the property that when multiplied by a vector, it produces the projection of that vector onto the subspace.

3. What is a subspace in linear algebra?

A subspace is a subset of a vector space that is closed under vector addition and scalar multiplication. This means that if you take any two vectors in the subspace and add them together, the result will also be in the subspace. Similarly, multiplying any vector in the subspace by a scalar will also result in a vector in the subspace.

4. How are projections and subspaces related?

Projections and subspaces are closely related because a projection is a transformation that maps a vector onto a subspace. The projection of a vector will always lie within the subspace, and the subspace can be thought of as the "target" of the projection.

5. What is the purpose of using projections and subspaces in linear algebra?

Projections and subspaces are useful in linear algebra for various reasons. They can be used to simplify calculations and make problems more manageable, as well as to better understand the structure of a vector space. They also have applications in fields such as data analysis, where they can be used to reduce the dimensionality of a dataset while still retaining important information.

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