Linear algebra and choosing training vector

In summary, the conversation revolves around understanding the construction of a specific matrix, G2, in an article referred to by the speaker. The speaker is confused about the left two partitions of the matrix and discusses their findings and logic in attempting to figure it out. After multiple days of trial and error, the speaker realizes that the matrix is for a real system, not a complex one, and the first partition of equation (21) provides the necessary elements for the first two partitions of G2. The speaker is both relieved and grateful for any additional insights or comments.
  • #1
perplexabot
Gold Member
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5
Hey all. So I have been reading this article and have a question I would like to ask. I will be referring to this article extensively so it would be kind of you to open it: http://www.ee.ucr.edu/~yhua/MILCOM_2013_Reprint.pdf

I believe reading the article is not required to answer my questions (i did personally read it tho up until where I am stuck), but I will refer to the equation numbers in the article instead of typing them out here.

So, on page 5 of the pdf, there is a matrix, G2, or equation number (22). I am basically trying to figure out how they constructed this matrix! Specifically the left two partitions.

  • Equation (15) gives the definition of G, and shows that G depends on u(k).
  • Equation (12) defines u(k) to be [u1T | u2T | ##\bar{g}## T | 1].
  • The two lines after equation (12) define u1T and u2T
  • Equation (5) defines ##\bar{g}## T, giT, and grT
  • Now that we have all the definitions stated we can go back to equation (22), or G2. It is stated in the two lines above equation (22) that m = 2 (m is the column size of ##\bar{g}## T). Right?
IT IS AT THIS POINT WHERE MY CONFUSION BEGINS
  • Now if ##\bar{g}## T contains only two elements that means, according to equation (5), giT, and grT would be scalars, right?
  • Continuing with this logic and going to the definition of u2T, the difference of the Kronecker product of two scalars, would be zero! would it not? Hence, u2T = 0
  • According to the previous bullet point, this would make the second column/partition (from the left) of equation (22) equal to 0, which contradicts what is shown in equation (22)
  • Doing the same with u1T and the first column/partition (from the left) of equation (22) would also yield a different answer.
That is my logic on this. I know I am doing something wrong, but I am not sure what it is. Please help me out here as I have been trying to understand this for a quite a bit. Thank you for read and your time : )
 
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  • #2
Hey! I don't know if anyone cares but I believe I got it! It was right in front of my face the whole time. So the G2 I was trying to find was for a real system (not complex), this required the use of equation (21)! I considered that earlier but what I thought was that since equation (21) had only 3 partitions while G2 had 4 partitions that equation doesn't work. It turns out tho that the first partition of equation (21) provided three elements when solved. These three elements covered the first two partitions of G2.

Damn, if feels good to figure something out after days of trial and error. Sorry to take your time and thank you for reading. I am still open to comments of course : )
 

1. What is linear algebra?

Linear algebra is a branch of mathematics that deals with the study of linear equations and their representations in vector spaces. It involves the use of algebraic operations to solve systems of linear equations and analyze geometric concepts such as vectors, matrices, and transformations.

2. How is linear algebra used in machine learning?

Linear algebra is a fundamental tool in machine learning as it provides a way to represent and manipulate data in a structured and efficient manner. It is used to perform operations such as dimensionality reduction, feature extraction, and classification, which are essential in training and optimizing machine learning models.

3. What is the importance of choosing training vectors in machine learning?

Choosing the right training vectors is crucial in machine learning as it directly affects the performance and accuracy of the model. Training vectors are the data points used to train the model, and they should represent the entire dataset in order to avoid bias and overfitting.

4. How do I select the most suitable training vectors for my model?

The process of selecting training vectors involves understanding the problem at hand and the type of data available. It is important to choose a diverse set of data points that are relevant to the problem and cover a wide range of features. Additionally, techniques such as cross-validation can be used to evaluate the performance of different training vectors.

5. Can linear algebra be used for other applications besides machine learning?

Yes, linear algebra has numerous applications in various fields such as physics, engineering, economics, and computer graphics. It is used to solve problems involving systems of equations, linear transformations, and optimization. It also provides a basis for understanding more complex mathematical concepts and theories.

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