Questions tagged [pca]
The pca tag has no usage guidance.
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Removing Phase shift in ECG signal
I have ECG signal sampled at 100Hz & using Python to remove noise, and generate a template signal from the collection of ECG from a total of almost 4700 signals. I have removed the noise from ECG ...
6
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1
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Discrete Cosine Transform (DCT) as the limit of Principal Component Analysis (PCA)
On the Wikipedia article about Discrete cosine transform it is said:
For strongly correlated Markov processes, the DCT can approach the compaction efficiency of the Karhunen-Loève transform (which is ...
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ICA and Gaussianity: A Misleading Example in the Book Konstantinos Koutroumbas, Sergios Theodoridis - Pattern Recognition
A book reports that ICA cannot be used if the independent components of the analyzed data are Gaussian (at most one can be Gaussian, but no other). However, in the same book, the following example is ...
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Pre-Processing Wi-Fi Channel State Information (CSI) Data
I was successfully able to collect some CSI data using the existing tool(s) on GitHub (https://github.com/StevenMHernandez/ESP32-CSI-Tool). The CSI data is a pair of imaginary and real number which ...
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Can Principal Component Analysis (PCA) Solve the Cocktail Party Problem?
I'm looking into the cocktail party problem and trying to figure out whether something like Principal Component Analysis is enough to separate out all the various voices at the cocktail party into its ...
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feature extraction techniques for iris recognition
I want to ask how I can divide feature extraction techniques to feature detectors and feature descriptors. I have big problem how to understand it. For example I can use Gabor filters (feature ...
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Reconstruct images from PCA reduced dimensions with NN
I was reading this Medium post and I had the idea to reconstruct the original images with a convolutional neural network instead of applying the inverse transform method. The problem is that I don't ...
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Principal Component Analysis definition
I have just learned about this method, so I am not very familiar with it. As far as I know, Principal Component Anlysis (aka PCA) is used to transform a vector $x$ that belongs to a space of $d$ ...
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Apply Principal Component Analysis (PCA) for RGB Images
I've implemented a method to compute PCA on grayscale images.
I haven't seen PCA on RGB images yet, which left me wondering if it is possible to perform it.
With RGB images, is PCA done for each ...
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How would PCA run on multivariate time-series data affect phase relationships across variables?
I am running PCA on a multivariate time-series dataset using observations across time (i.e. w/out time as an explicit variable) as the design matrix. Given this setup, I've found that it is difficult ...
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Implementation of PCA for hyper-spectral Image Processing
I have been studying the concept of PCA and its implementation for dimensionality reduction for more than 1 month. My goal is to classify a hyperspectral image using sparse representation by the ...
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Is it possible to weight the high frequency components of a signal to give high frequecy components greater overall power in the total signal?
I have a multivariate time-series dataset, and would like to run PCA on my dataset to reduce the number of variables I input into a time-series model. I am concerned that running PCA may end up ...
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How Can PCA Be Used in Image Analysis [closed]
I am still a not how PCA can be used in image analysis and where is it is mostly used. For example how can PCA be used in order to differentiate between different faces? Can you please mention other ...
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What Is the Difference Between PCA and Karhunen Loeve (KL) Transform?
I have been reading about Karhunen-Loeve (KL) transform. I see that when it is used to reduce dimension the procedure is identical to PCA, that is, for both methods the covariance matrix of the data ...
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Sourse separation from known underdetermined mixing matrix
How to recover uncorrelated N sources from given N-1 signals and known mixing matrix M, (e.g. 9x8 matrix)?
If I just use pseudo-inverse matrix M+, my source estimates are correlated with each other ...
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