Least Square Estimator for Matrices: Bill's Problem

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Discussion Overview

The discussion revolves around finding the least square estimator for a matrix B given matrices A and C, where A is expressed as the product of B and C. The scope includes theoretical exploration and mathematical reasoning related to matrix norms and projection operators.

Discussion Character

  • Exploratory
  • Mathematical reasoning

Main Points Raised

  • Bill introduces the problem of estimating matrix B from known matrices A and C, noting that the solution is straightforward for vectors but unclear for matrices.
  • One participant questions how the "error" is defined in the context of least squares, suggesting that without a clear definition, the least squares approach lacks specificity.
  • Bill proposes using the matrix L2 norm to minimize the sum of squared differences between known matrix pairs A and C, referencing a paper for further context.
  • Another participant suggests that if the space of matrices is a Hilbert space, the solution may involve an orthogonal projection of B onto the subspace spanned by A and C.
  • Bill acknowledges the existence of a projection operator and mentions the trace in relation to this concept.
  • Bill later concludes that the problem can be simplified by breaking matrix B into rows, transforming it into several ordinary least squares problems, each minimizing the squared differences for the respective rows.

Areas of Agreement / Disagreement

The discussion features multiple viewpoints regarding the definition of error and the approach to solving the problem, indicating that no consensus has been reached on a singular method or definition.

Contextual Notes

Participants express uncertainty regarding the definition of error in the least squares context and the implications of using matrix norms and projections in the solution process.

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I recently came across the following interesting problem.

Suppose A = BC where A,B, and C are matrices. We know a ton of A's and their corresponding C's. We want the least square estimator of B.

When A and C are vectors the solution is well known.

But what is the solution when they are matrices?

Thanks
Bill
 
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How do you intend to define the "error" between the observed and predicted values? Until that is defined, "least squares" doesn't describe a specific criteria.
 
Isn't there an equivalent of a perp projection operator in your space of matrices ?If this space is a Hilbert space, then, AFAIK, the general solution to this problem in a Hilbert space is the ortho. projection of B onto the subspace spanned by A,C.
 
WWGD said:
Isn't there an equivalent of a perp projection operator in your space of matrices ?If this space is a Hilbert space, then, AFAIK, the general solution to this problem in a Hilbert space is the ortho. projection of B onto the subspace spanned by A,C.

Yes there is - its the trace. I will think about that one.

Thanks
Bill
 
Hi Guys

Thanks for all the help.

Finally nutted it out. As usual I was on the wrong track. It's simply a matter by blocking the problem and reducing it to a number of ordinary least squares problems. Break B into rows Bj so you get the usual least squares problems ||Aji - BjCi||^2. The minimum is the minimum of each of these separate problems.

Thanks
Bill
 

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