To successfully compress the data using Compressive Sensing method, I need to have sparse vector, theoretically a vector is sparse if the entries of the vector has many zero or nearly zero. My question is how do you determined the maximum value of the nearly zero?
I tried it to make sparse matrix on Matlab. Let say I have 64 data which is a dense vector. After I tried to make my vector sparse using FFT I took the absolute value from it (I believe it's easier to identified the zero or nearly zero entries). My original value has minimum value of 1 and maximum value of 26 my sparse value has minimum value 7.3245 and maximum value of 602. based on my sparse vector, I don't think there is an entry it's nearly zero