My problem is similar to this one but I am looking for a different solution: (so if it should be merged just let me know). Measuring what's 'lost' in PCA dimensionality reduction? I my ...
I am very interested in the Laplacian eigenmaps method. Currently, I am using it to do dimension reduction for my medical data sets. However, I have run into a problem using the method. For ...
$X = AS$ where $A$ is my mixing matrix and each column of $S$ represents my sources. $X$ is the data I observe. If the columns of $S$ are independent and Gaussian, will the components of PCA be ...
In PCA eigenvalues determine the order of components. In ICA I am using kurtosis to obtain the ordering. What are some accepted methods to assess the number, (given I have the order) of components ...