Relation between Gram matrix distributions

Your Name]In summary, the question is about the relation between the distribution of two matrices, HH and HHH, where H stands for the Hermittian transposition. The intuition is that they both follow the complex Wishart distribution with the same parameters. This is proven by using the properties of Hermittian transposition and the complex Wishart distribution. Both matrices follow the complex Wishart distribution with parameters \sigma and 2n degrees of freedom.
  • #1
nikozm
54
0
Hello,

Assume that H is a n \times m matrix with i.i.d. complex Gaussian entries each with zero mean and variance \sigma. Also, let n>=m. I ' m interested in finding the relation between the distribution of HHH and HHH, where H stands for the Hermittian transposition. I anticipate that both follow the complex wishart distribution with the same parameters (since they share the same nonzero eigenvalues), but I m not sure about this.

Any ideas ? Thanks in advance..
 
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  • #2


Hi there,

Thank you for your interesting question. You are correct in your intuition that both HHH and HHH follow the complex Wishart distribution with the same parameters. This can be proven using the properties of the Hermittian transposition and the complex Wishart distribution.

First, let's define some notation. Let HH be a complex Gaussian matrix with zero mean and variance \sigma, as described in the forum post. We can write HH as HH = XX^H, where X is a complex Gaussian matrix with zero mean and variance \sigma. The matrix X^H is the conjugate transpose of X. We can also define HHH as HHH = YY^H, where Y is the Hermittian transposition of X.

Now, the complex Wishart distribution is defined as the distribution of HH = XX^H, where X is a p \times n matrix with i.i.d. complex Gaussian entries each with zero mean and variance \sigma. This means that both HH and HHH follow the same distribution, since they are both of the form XX^H.

Furthermore, the nonzero eigenvalues of a complex Wishart matrix are distributed according to the chi-square distribution with 2n degrees of freedom, where n is the number of rows of the matrix. Since both HH and HHH have the same nonzero eigenvalues, they must also follow the same chi-square distribution with 2n degrees of freedom.

In conclusion, both HH and HHH follow the complex Wishart distribution with the same parameters \sigma and 2n degrees of freedom. I hope this helps answer your question. Let me know if you have any further doubts or if you need any clarification.

 

Related to Relation between Gram matrix distributions

What is a Gram matrix?

A Gram matrix is a square matrix that is obtained by multiplying a set of vectors by their transpose. It is often used in linear algebra and statistics to represent the inner products of vectors.

What is the significance of the Gram matrix in machine learning?

In machine learning, the Gram matrix is used to calculate the similarity between two sets of data points. It is also a key component in many machine learning algorithms, such as support vector machines and principal component analysis.

What is the relation between Gram matrix distributions and kernel functions?

Kernel functions are used to map data into a higher-dimensional space, making it easier to find a linear separation between data points. The Gram matrix is used to calculate the inner product between these mapped data points, which is essential in many kernel-based machine learning algorithms.

Can the distribution of a Gram matrix be used to determine the linear separability of data?

Yes, the distribution of values in a Gram matrix can provide insights into the linear separability of data. For example, if the majority of values in the matrix are close to zero, it may indicate that the data is not easily separable by a linear boundary.

What are some applications of studying the relation between Gram matrix distributions?

Studying the relation between Gram matrix distributions can provide insights into the structure and separability of data, which can be useful in various fields such as machine learning, data mining, and pattern recognition. It can also help in understanding the performance and limitations of different kernel functions in machine learning algorithms.

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