Questions tagged [pca]

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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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1answer
28 views

Compression of Multivariate Phase Signals

I have a data matrix $X$ whose columns are composed of instantaneous phases of recordings from different electrodes. In other words, each column keeps a periodic sawtooth signal with a range from $[-\...
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2answers
226 views

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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23 views

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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20 views

What components that I should have in using PCA to denoise noisy ECG signal?

I know that I have to have more than one component to use PCA method to process some signals. But I only have one noisy ECG signal that has to be denoised. What other components in PCA beside the ...
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22 views

Interpretation of Complex modes in Frequency Domain Decomposition

When I do a Frequency Domain Decomposition (FDD) I always get mode shapes with significant imaginary components. Let's say I see a strong modal response. Still have strong complex nature. When I do a ...
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1answer
65 views

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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1answer
110 views

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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4answers
4k views

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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17 views

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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36 views

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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39 views

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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1answer
440 views

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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1answer
2k views

What Is the Difference Between PCA and Karhunen Loeve (KL Transform)?

I have been reading about Karhunen-Loeve or also known as KL transform and I see that when it is used to reduce dimension the procedure is identical to PCA, that is, for both methods the covariance ...
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33 views

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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1answer
253 views

Finding filaments in an image

I am at the moment working on images such as this one: What you see are filamentous structures / bundles. Other images coming from slightly different experiments could have more sparse / thick / ...
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1answer
91 views

How to express one image in terms of another one

I have two (black and white) images of identical size - let's say 128x128 pixels. I'm interested in expressing Im2 in terms of ...
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1answer
852 views

PCA to reduce dimensionality to 99% variance

I'm attempting to use PCA to reduce the dimensionality of a dataset I have. I want to explain 99% of the variance in the dataset, and I think I've been able to determine that, but I'm unsure what I ...
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3answers
4k views

Principal Component Analysis (PCA) on Convolutional Neural Network (CNN) Features

Please, I have a question regarding PCA and features which are extracted from a convolutional layer. link if we have a test dataset , and we extract all conv features of all images at test dataset ...
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1answer
205 views

Whitening signal vs. whitening its DFT

A whitening transformation (PCA) is simply a rotation into a space in which variables become uncorrelated. Because a DFT is a transformation into a coordinate space of orthogonal frequency components,...
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2answers
48 views

Face Classification. Is it OK to only use geometric features?

I am trying to teach myself the basics of facial recognition. I see that some resources use just distances between some points on the face (e.g., distance between 2 eyes, eyes to nose, etc). Some ...
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1answer
332 views

What Is the Significance of a Large Residual When Applying Principal Component Analysis?

I am using Matlab function PCA (principal component analysis) to reduce the dimensionality of a data set with approximately 20 000 observations x 100 dimensions. After having obtained the principal ...
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2answers
1k views

MUSIC algorithm derivation

Setup Suppose we have a complex $L\times 1$ signal $\mathbf{x}$ with two tones at (unknown) frequencies and phases defined as: $$ x_n = A_1 e^{j \omega_1n + \varphi_1} + A_2 e^{j \omega_2n + \...