Tagged Questions
0
votes
0answers
53 views
Two variable PCA using gradients
Let $x$ and $y$ be two random variables. Using principal component analysis (PCA), I can find a linear projection making the two variables uncorrelated. PCA solves this problem through an eigenvalue ...
5
votes
2answers
110 views
Usage of inverse Laplace transform
At my current study level in college, use of inverse Laplace transform is not mentioned well - textbooks say "use tables." So, can anyone show me how to use inverse Lapalce transform? And also proof?
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1
vote
1answer
69 views
What does it mean to convolve a matrix with a kernel?
I have a Matrix, M, of dimensions width x height. The problem is to apply the [-1, 0, 1] filter along the x and y axis (i.e. convolve the image with [-1, 0, 1] kernel along horizontal and vertical ...
1
vote
1answer
92 views
Should mean be subtracted before convolution?
Suppose I have a signal in the form of N real numbers. I am searching for a continuous pattern within this signal. The pattern is represented by M real numbers. (M < N)
To find the pattern's ...
3
votes
2answers
1k views
How do I - exactly - project a vector, onto a subspace?…
I am trying to understand how - exactly - I go about projecting a vector onto a subspace.
Now, I know enough about linear algebra to know about projections, dot products, spans, etc etc, so I am not ...
1
vote
0answers
193 views
Fast Walsh–Hadamard transform generalization for non-power-of-two orders?
I have to process vectors through a Hadamard matrix of order N.
If N is a power of 2, I can use the Fast Walsh–Hadamard transform; but if N is not a power of two (for instance, N=12), it is not ...
2
votes
0answers
124 views
Singular Value Decomposition
I want to decompose an image $A$ using the Discrete Wavelet Transform and then find the singular values, $S$, such that $A=USV$. I will then do the same to another image such that $B=USV$. I will ...
2
votes
2answers
283 views
normalization of a linear combination
I'm a newbie at this forum, so I hope, that this question is not so silly.
Let's have some filter $F$, which is a linear combination, thus
$F = \sum_{i=0}^{i=N}\alpha_ib_i$, where $\alpha_i$ are ...
9
votes
2answers
256 views
Fast computation/estimation of the nuclear norm of a matrix
The nuclear norm of a matrix is defined as the sum of its singular values, as given by the Singular Value Decomposition of the matrix itself. It is of central importance in Signal Processing and ...
