Understanding the Limitations of the Projection onto a Subspace Equation

In summary, the equation Projv(x) = A(ATA)-1ATx does not reduce to Projv(x) = IIx, despite the reasoning that (ATA)-1 = A-1(AT)-1. This is because A will be rectangular if projecting onto a subspace, making its inverse non-existent. This can be seen in various sources, such as the Khan Academy video, Wikipedia, and the book "Matrix Analysis and Applied Linear Algebra." The problem lies in the fact that A is a column vector when projecting onto a line, making its inverse non-existent.
  • #1
fredsmithsfc
3
0
The Projv(x) = A(ATA)-1ATx

I'm puzzled why this equation doesn't reduce to Projv(x) = IIx

since (ATA)-1 = A-1(AT)-1 so that should mean that A(ATA)-1AT = AA-1(AT)-1AT = II

What is wrong with my reasoning?

Thanks.
 
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  • #2
It doesn't look wrong to me. Where did you get the idea that the first expression is a projection operator?
 
  • #3
The problem is that A will be rectangular (non-square) if you are projecting onto a subspace, and thus its inverse does not exist (e.g. A is a column vector for projection onto a line).
 
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  • #4
Fredrik said:
It doesn't look wrong to me. Where did you get the idea that the first expression is a projection operator?

Hi Fredrik,

I first saw it in the Khan Academy Linear Algebra video: "Lin Alg: A Projection onto a Subspace is a Linear Transformation" which is at this link: http://www.khanacademy.org/video/lin-alg--a-projection-onto-a-subspace-is-a-linear-transforma?playlist=Linear%20Algebra

But I also found it at Wikipedia here: http://en.wikipedia.org/wiki/Projection_(linear_algebra)

and in the book "Matrix Analysis and Applied Linear Algebra" by Carl Meyer on page 430

--
 
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  • #5
monea83 said:
The problem is that A will be rectangular (non-square) if you are projecting onto a subspace, and thus its inverse does not exist (e.g. A is a column vector for projection onto a line).

That makes sense. Thanks.
 

What is projection onto a subspace?

Projection onto a subspace is a mathematical operation that involves finding the shortest distance between a vector and a given subspace. This is done by finding the vector that lies on the subspace and is closest to the original vector.

How is projection onto a subspace calculated?

The projection of a vector onto a subspace can be calculated using the dot product formula. This involves finding the dot product of the original vector and a unit vector that lies on the subspace.

What is the purpose of projection onto a subspace?

The purpose of projection onto a subspace is to simplify a problem by reducing the dimensionality of the vector space. It also helps to find the closest approximation of a vector in a subspace, which can be useful in various applications such as machine learning and data analysis.

Is projection onto a subspace reversible?

No, projection onto a subspace is not reversible. This is because information is lost during the projection process, making it impossible to retrieve the original vector from its projection onto a subspace.

Can projection onto a subspace be applied to higher dimensions?

Yes, projection onto a subspace can be applied to vectors of any dimension. The formula and process remain the same, but the calculations become more complex as the dimensionality increases.

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