Questions tagged [pca]

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Is it feasible to perform PCA on large size images prior to classification?

I have a dataset composed of $970$ images with size $256\times256$, so I have a data matrix $X \in \mathbb{R}^{970\times65536}$. My idea is to compute the PCA transformation in the training phase ...
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How to convert images in a testing folder into a one dimension vector?

I have a sample of 200 images in a folder called training, The dimensions of each is 112*92. I have a problem reading the ...
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Doing ICA analysis on MEG data with 248 channels

I am trying to localize sources of brain activity using MEG data. I first want to compute ICA and then localize independent components of interest. However, I sometimes have trouble making a decision ...
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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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10 votes
2 answers
585 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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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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1 answer
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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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3 votes
1 answer
117 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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6 votes
4 answers
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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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4 votes
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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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1 vote
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36 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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1 answer
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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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1 answer
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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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1 answer
921 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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6 votes
3 answers
6k 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 based on Faster R-CNN features for Instance Search if we have a test dataset , and we extract all ...
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2 votes
1 answer
222 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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1 vote
2 answers
49 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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1 vote
1 answer
370 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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4 votes
2 answers
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 + \...
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