Change in eigenvalues due to perturbation to a correlation matrix
Let $A$ be a $m times n$ matrix defined as
$ A = Big[frac{a_1}{|a_1|} cdots frac{a_n}{|a_n|}Big]$ and $a_k in mathbb{R}^{mtimes 1}$ where $k in [1,dots,n]$.
Now, we define a correlation matrix $R = A^TA$ where each diagonal element is $1$ and it is a symmetric matrix. The trace of $R$, i.e., $mathbb{Tr}(R) = n$.
All non-diagonal elements of $R$ represents the correlation among the columns of $A$. We define them by correlation-coefficients
$rho_{jk} = Big(frac{a_j}{|a_j|}Big)^TBig(frac{a_k}{|a_k|}Big)$
which satisfy $-1 leq rho_{jk} leq 1$.
In my present work, I modify each column of $A$ such that the correlation among the columns of $A$ increases. Consequently, the correlation-coefficients also increases proportionally in $R$. I am interested to comment on the change in eigenvalues of $R$ with increase in correlation-coefficients.
Numerically, I observed that only largest eigenvalue of $R$ increases whereas rest of the eigenvalues decreases. But, I am unable to verify this phenomenon theoretically. Therefore, I ask you here for a hint to proceed my investigation further.
More precisely, the claim is:
Let $lambda$ be the set of eigenvalues of $R$ where $lambda_1 geq cdots geq lambda_n geq 0$ and $hat{lambda}$ be the set of eigenvalues of $hat{R}$ where $hat{lambda}_1 geq cdots geq hat{lambda}_n geq 0$. Assume that the correlation-coefficients in $hat{R}$ satisfy
$$
hat{rho}_{jk} geq rho_{jk} quad {j,k} in [1,cdots,n] quad text{and} quad jneq k.
$$
Moreover the trace of correlation matrices remains same, i.e.,
$$
mathbb{Tr}(hat{R}) = mathbb{Tr}({R}) = n.
$$
Consequently, we claim that the eigenvalues of $hat{R}$ and $R$ satisfy the following inequalities:
$$
hat{lambda}_1 geq lambda_1 quad text{and} quad hat{lambda}_i leq lambda_i quad iin[2,dots,n].
$$
Example:
Suppose, all columns of $A$ are orthonormal. This implies that the resulting correlation matrix would be an identity matrix and in this case, all eigenvalues are equal to $1$.
Now, suppose all columns are linearly dependent by a positive factor. This implies that the all correlation-coefficients is equal to 1 and the resulting correlation matrix is a rank-1 matrix, i.e., $mathbb{1}mathbb{1}^T$ where the largest eigenvalue is $n$ and rest of the eigenvalues are zero.
In this example, the largest eigenvalue increases from $1$ to $n$ when correlation matrix changes from the identity matrix to the rank-1 matrix. On the other hand, rest of the eigenvalues decreases from $1$ to $0$.
In order to prove the above mentioned claim, will it be sufficient:
if we can show that the largest eigenvalue path from the identity matrix to a matrix of ones, i.e., $mathbb{1}mathbb{1}^T$ is monotonically non-decreasing. Here, we will change only off-diagonal elements which always lies between -1 to 1. We also establish similar behaviour for rest of the eigenvalues ?
Can you comment on this approach? If you think, it could be a right direction. Do you have any suggestion how should I start to prove/disprove the claim?
linear-algebra matrices eigenvalues-eigenvectors linear-transformations perturbation-theory
asked Dec 9 at 8:38
hari
63
add a comment |
Let $A$ be a $m times n$ matrix defined as
$ A = Big[frac{a_1}{|a_1|} cdots frac{a_n}{|a_n|}Big]$ and $a_k in mathbb{R}^{mtimes 1}$ where $k in [1,dots,n]$.
Now, we define a correlation matrix $R = A^TA$ where each diagonal element is $1$ and it is a symmetric matrix. The trace of $R$, i.e., $mathbb{Tr}(R) = n$.
All non-diagonal elements of $R$ represents the correlation among the columns of $A$. We define them by correlation-coefficients
$rho_{jk} = Big(frac{a_j}{|a_j|}Big)^TBig(frac{a_k}{|a_k|}Big)$
which satisfy $-1 leq rho_{jk} leq 1$.
In my present work, I modify each column of $A$ such that the correlation among the columns of $A$ increases. Consequently, the correlation-coefficients also increases proportionally in $R$. I am interested to comment on the change in eigenvalues of $R$ with increase in correlation-coefficients.
Numerically, I observed that only largest eigenvalue of $R$ increases whereas rest of the eigenvalues decreases. But, I am unable to verify this phenomenon theoretically. Therefore, I ask you here for a hint to proceed my investigation further.
More precisely, the claim is:
Let $lambda$ be the set of eigenvalues of $R$ where $lambda_1 geq cdots geq lambda_n geq 0$ and $hat{lambda}$ be the set of eigenvalues of $hat{R}$ where $hat{lambda}_1 geq cdots geq hat{lambda}_n geq 0$. Assume that the correlation-coefficients in $hat{R}$ satisfy
$$
hat{rho}_{jk} geq rho_{jk} quad {j,k} in [1,cdots,n] quad text{and} quad jneq k.
$$
Moreover the trace of correlation matrices remains same, i.e.,
$$
mathbb{Tr}(hat{R}) = mathbb{Tr}({R}) = n.
$$
Consequently, we claim that the eigenvalues of $hat{R}$ and $R$ satisfy the following inequalities:
$$
hat{lambda}_1 geq lambda_1 quad text{and} quad hat{lambda}_i leq lambda_i quad iin[2,dots,n].
$$
Example:
Suppose, all columns of $A$ are orthonormal. This implies that the resulting correlation matrix would be an identity matrix and in this case, all eigenvalues are equal to $1$.
Now, suppose all columns are linearly dependent by a positive factor. This implies that the all correlation-coefficients is equal to 1 and the resulting correlation matrix is a rank-1 matrix, i.e., $mathbb{1}mathbb{1}^T$ where the largest eigenvalue is $n$ and rest of the eigenvalues are zero.
In this example, the largest eigenvalue increases from $1$ to $n$ when correlation matrix changes from the identity matrix to the rank-1 matrix. On the other hand, rest of the eigenvalues decreases from $1$ to $0$.
In order to prove the above mentioned claim, will it be sufficient:
if we can show that the largest eigenvalue path from the identity matrix to a matrix of ones, i.e., $mathbb{1}mathbb{1}^T$ is monotonically non-decreasing. Here, we will change only off-diagonal elements which always lies between -1 to 1. We also establish similar behaviour for rest of the eigenvalues ?
Can you comment on this approach? If you think, it could be a right direction. Do you have any suggestion how should I start to prove/disprove the claim?
linear-algebra matrices eigenvalues-eigenvectors linear-transformations perturbation-theory
asked Dec 9 at 8:38
hari
63
It is not clear to me if your allowed perturbation preserves the $a_i/|a_i|$ condition. If it does, that you know that the trace is still $n$ under the perturbation and hence the sum of eigenvalues is unchanged. In the 2d example that you presented, How do you know that $delta>0$? A negative $delta$ would invalidate your claim.
– user617446
Dec 9 at 12:53
@user617446 Yes, the allowed perturbation preserves the $a_i/|a_i|$ condition. This is always ensured. You are right, in this condition, sum of eigenvalues is unchanged and somehow I should utilize this information to prove my arguments. In the 2nd example, $delta$ is positive by construction. This signify that the correlation among all columns of $A$ are increasing.
– hari
Dec 9 at 15:32
add a comment |
Let $A$ be a $m times n$ matrix defined as
$ A = Big[frac{a_1}{|a_1|} cdots frac{a_n}{|a_n|}Big]$ and $a_k in mathbb{R}^{mtimes 1}$ where $k in [1,dots,n]$.
Now, we define a correlation matrix $R = A^TA$ where each diagonal element is $1$ and it is a symmetric matrix. The trace of $R$, i.e., $mathbb{Tr}(R) = n$.
All non-diagonal elements of $R$ represents the correlation among the columns of $A$. We define them by correlation-coefficients
$rho_{jk} = Big(frac{a_j}{|a_j|}Big)^TBig(frac{a_k}{|a_k|}Big)$
which satisfy $-1 leq rho_{jk} leq 1$.
In my present work, I modify each column of $A$ such that the correlation among the columns of $A$ increases. Consequently, the correlation-coefficients also increases proportionally in $R$. I am interested to comment on the change in eigenvalues of $R$ with increase in correlation-coefficients.
Numerically, I observed that only largest eigenvalue of $R$ increases whereas rest of the eigenvalues decreases. But, I am unable to verify this phenomenon theoretically. Therefore, I ask you here for a hint to proceed my investigation further.
More precisely, the claim is:
Let $lambda$ be the set of eigenvalues of $R$ where $lambda_1 geq cdots geq lambda_n geq 0$ and $hat{lambda}$ be the set of eigenvalues of $hat{R}$ where $hat{lambda}_1 geq cdots geq hat{lambda}_n geq 0$. Assume that the correlation-coefficients in $hat{R}$ satisfy
$$
hat{rho}_{jk} geq rho_{jk} quad {j,k} in [1,cdots,n] quad text{and} quad jneq k.
$$
Moreover the trace of correlation matrices remains same, i.e.,
$$
mathbb{Tr}(hat{R}) = mathbb{Tr}({R}) = n.
$$
Consequently, we claim that the eigenvalues of $hat{R}$ and $R$ satisfy the following inequalities:
$$
hat{lambda}_1 geq lambda_1 quad text{and} quad hat{lambda}_i leq lambda_i quad iin[2,dots,n].
$$
Example:
Suppose, all columns of $A$ are orthonormal. This implies that the resulting correlation matrix would be an identity matrix and in this case, all eigenvalues are equal to $1$.
Now, suppose all columns are linearly dependent by a positive factor. This implies that the all correlation-coefficients is equal to 1 and the resulting correlation matrix is a rank-1 matrix, i.e., $mathbb{1}mathbb{1}^T$ where the largest eigenvalue is $n$ and rest of the eigenvalues are zero.
In this example, the largest eigenvalue increases from $1$ to $n$ when correlation matrix changes from the identity matrix to the rank-1 matrix. On the other hand, rest of the eigenvalues decreases from $1$ to $0$.
In order to prove the above mentioned claim, will it be sufficient:
if we can show that the largest eigenvalue path from the identity matrix to a matrix of ones, i.e., $mathbb{1}mathbb{1}^T$ is monotonically non-decreasing. Here, we will change only off-diagonal elements which always lies between -1 to 1. We also establish similar behaviour for rest of the eigenvalues ?
Can you comment on this approach? If you think, it could be a right direction. Do you have any suggestion how should I start to prove/disprove the claim?
linear-algebra matrices eigenvalues-eigenvectors linear-transformations perturbation-theory
asked Dec 9 at 8:38
hari
63
Let $A$ be a $m times n$ matrix defined as
$ A = Big[frac{a_1}{|a_1|} cdots frac{a_n}{|a_n|}Big]$ and $a_k in mathbb{R}^{mtimes 1}$ where $k in [1,dots,n]$.
Now, we define a correlation matrix $R = A^TA$ where each diagonal element is $1$ and it is a symmetric matrix. The trace of $R$, i.e., $mathbb{Tr}(R) = n$.
All non-diagonal elements of $R$ represents the correlation among the columns of $A$. We define them by correlation-coefficients
$rho_{jk} = Big(frac{a_j}{|a_j|}Big)^TBig(frac{a_k}{|a_k|}Big)$
which satisfy $-1 leq rho_{jk} leq 1$.
In my present work, I modify each column of $A$ such that the correlation among the columns of $A$ increases. Consequently, the correlation-coefficients also increases proportionally in $R$. I am interested to comment on the change in eigenvalues of $R$ with increase in correlation-coefficients.
Numerically, I observed that only largest eigenvalue of $R$ increases whereas rest of the eigenvalues decreases. But, I am unable to verify this phenomenon theoretically. Therefore, I ask you here for a hint to proceed my investigation further.
More precisely, the claim is:
Let $lambda$ be the set of eigenvalues of $R$ where $lambda_1 geq cdots geq lambda_n geq 0$ and $hat{lambda}$ be the set of eigenvalues of $hat{R}$ where $hat{lambda}_1 geq cdots geq hat{lambda}_n geq 0$. Assume that the correlation-coefficients in $hat{R}$ satisfy
$$
hat{rho}_{jk} geq rho_{jk} quad {j,k} in [1,cdots,n] quad text{and} quad jneq k.
$$
Moreover the trace of correlation matrices remains same, i.e.,
$$
mathbb{Tr}(hat{R}) = mathbb{Tr}({R}) = n.
$$
Consequently, we claim that the eigenvalues of $hat{R}$ and $R$ satisfy the following inequalities:
$$
hat{lambda}_1 geq lambda_1 quad text{and} quad hat{lambda}_i leq lambda_i quad iin[2,dots,n].
$$
Example:
Suppose, all columns of $A$ are orthonormal. This implies that the resulting correlation matrix would be an identity matrix and in this case, all eigenvalues are equal to $1$.
Now, suppose all columns are linearly dependent by a positive factor. This implies that the all correlation-coefficients is equal to 1 and the resulting correlation matrix is a rank-1 matrix, i.e., $mathbb{1}mathbb{1}^T$ where the largest eigenvalue is $n$ and rest of the eigenvalues are zero.
In this example, the largest eigenvalue increases from $1$ to $n$ when correlation matrix changes from the identity matrix to the rank-1 matrix. On the other hand, rest of the eigenvalues decreases from $1$ to $0$.
In order to prove the above mentioned claim, will it be sufficient:
if we can show that the largest eigenvalue path from the identity matrix to a matrix of ones, i.e., $mathbb{1}mathbb{1}^T$ is monotonically non-decreasing. Here, we will change only off-diagonal elements which always lies between -1 to 1. We also establish similar behaviour for rest of the eigenvalues ?
Can you comment on this approach? If you think, it could be a right direction. Do you have any suggestion how should I start to prove/disprove the claim?
linear-algebra matrices eigenvalues-eigenvectors linear-transformations perturbation-theory
linear-algebra matrices eigenvalues-eigenvectors linear-transformations perturbation-theory
asked Dec 9 at 8:38
hari
63
asked Dec 9 at 8:38
hari
63
edited Dec 10 at 1:50
asked Dec 9 at 8:38
hari
63
asked Dec 9 at 8:38
hari
63
asked Dec 9 at 8:38
hari
63
63
It is not clear to me if your allowed perturbation preserves the $a_i/|a_i|$ condition. If it does, that you know that the trace is still $n$ under the perturbation and hence the sum of eigenvalues is unchanged. In the 2d example that you presented, How do you know that $delta>0$? A negative $delta$ would invalidate your claim.
– user617446
Dec 9 at 12:53
@user617446 Yes, the allowed perturbation preserves the $a_i/|a_i|$ condition. This is always ensured. You are right, in this condition, sum of eigenvalues is unchanged and somehow I should utilize this information to prove my arguments. In the 2nd example, $delta$ is positive by construction. This signify that the correlation among all columns of $A$ are increasing.
– hari
Dec 9 at 15:32
add a comment |
It is not clear to me if your allowed perturbation preserves the $a_i/|a_i|$ condition. If it does, that you know that the trace is still $n$ under the perturbation and hence the sum of eigenvalues is unchanged. In the 2d example that you presented, How do you know that $delta>0$? A negative $delta$ would invalidate your claim.
– user617446
Dec 9 at 12:53
@user617446 Yes, the allowed perturbation preserves the $a_i/|a_i|$ condition. This is always ensured. You are right, in this condition, sum of eigenvalues is unchanged and somehow I should utilize this information to prove my arguments. In the 2nd example, $delta$ is positive by construction. This signify that the correlation among all columns of $A$ are increasing.
– hari
Dec 9 at 15:32
It is not clear to me if your allowed perturbation preserves the $a_i/|a_i|$ condition. If it does, that you know that the trace is still $n$ under the perturbation and hence the sum of eigenvalues is unchanged. In the 2d example that you presented, How do you know that $delta>0$? A negative $delta$ would invalidate your claim.
– user617446
Dec 9 at 12:53
It is not clear to me if your allowed perturbation preserves the $a_i/|a_i|$ condition. If it does, that you know that the trace is still $n$ under the perturbation and hence the sum of eigenvalues is unchanged. In the 2d example that you presented, How do you know that $delta>0$? A negative $delta$ would invalidate your claim.
– user617446
Dec 9 at 12:53
@user617446 Yes, the allowed perturbation preserves the $a_i/|a_i|$ condition. This is always ensured. You are right, in this condition, sum of eigenvalues is unchanged and somehow I should utilize this information to prove my arguments. In the 2nd example, $delta$ is positive by construction. This signify that the correlation among all columns of $A$ are increasing.
– hari
Dec 9 at 15:32
@user617446 Yes, the allowed perturbation preserves the $a_i/|a_i|$ condition. This is always ensured. You are right, in this condition, sum of eigenvalues is unchanged and somehow I should utilize this information to prove my arguments. In the 2nd example, $delta$ is positive by construction. This signify that the correlation among all columns of $A$ are increasing.
– hari
Dec 9 at 15:32
add a comment |
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It is not clear to me if your allowed perturbation preserves the $a_i/|a_i|$ condition. If it does, that you know that the trace is still $n$ under the perturbation and hence the sum of eigenvalues is unchanged. In the 2d example that you presented, How do you know that $delta>0$? A negative $delta$ would invalidate your claim.
– user617446
Dec 9 at 12:53
@user617446 Yes, the allowed perturbation preserves the $a_i/|a_i|$ condition. This is always ensured. You are right, in this condition, sum of eigenvalues is unchanged and somehow I should utilize this information to prove my arguments. In the 2nd example, $delta$ is positive by construction. This signify that the correlation among all columns of $A$ are increasing.
– hari
Dec 9 at 15:32