# Questions tagged [principal-component-analysis]

For questions related to principal component analysis (PCA), which is commonly used in machine learning for dimensionality reduction.

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### What are examples of approaches to dimensionality reduction of feature vectors?

Given a pre-trained CNN model, I extract feature vector of images in reference and query dataset with several thousands of elements. I would like to apply some augmentation techniques to reduce the ...
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### When using PCA for dimensionality reduction of the feature vectors to speed up learning, how do I know that I'm not letting the model overfit?

I'm following Andrew Ng's course for Machine Learning and I just don't quite understand the following. Using PCA to speed up learning Using PCA to reduce the number of features, thus lowering the ...
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### Do the eigenvectors represent the original features?

I've got a test dataset with 4 features and the PCA produces a set of 4 eigenvectors, e.g., ...
53 views

### Looking for the proper algorithm to compress many lowres images of nearby locations

I have an optimization problem that I'm looking for the right algorithm to solve. What I have: A large set of low-res 360 images that were taken on a regular grid within a certain area. each of these ...
157 views

### Why does PCA of the vertices of a hexagon result in principal components of equal length?

I do PCA on the data points placed in the corners of a hexagon, and get the following principal components: The PCA variance is $0.6$ and is the same for each component. Why is that? Shouldn't it be ...
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### Multiple-dimension scaling (MDS) objective for MDS and PCA

The following is the MDS Objective. Let's think of a senario where I apply MDS with/from the solution I obtained from PCA. Then I calculate the objective function on the initial PCA solution and MDS ...
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### What is the difference between principal component analysis and singular value decomposition in image processing?

What is the difference between principal component analysis and singular value decomposition in image processing? Which one performs better, and why?
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### Why does PCA work well while the total variance retained is small?

I'm learning machine learning by looking through other people's kernel on Kaggle, specifically this Mushroom Classification kernel. The author first applied PCA to the transformed indicator matrix. He ...
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### Is it theoretically possible (or impossible) that principal component analysis worsens the performance of the model?

In case I had a prediction model and decided to add a PCA step prior to the model, is it theoretically possible/impossible that the number of output dimensions that is better for all tests may perform ...
42 views

### Why Autoencoder Weights Are Not Always Tied

To me, tying weights in an autoencoder makes sense if we think of the auto encoder as doing PCA. Why in any situation would it make sense to not tie the weights? If we don't tie the weights, would it ...
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### Is there a way to select the subset of most important features using PCA?

Is there a way to select the most important features using PCA? I am not looking for the principal components with the highest scores but a subset of the original features.
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### Can I take some conclusion from two dimension PCA

I have a table of 140 features and try to predict binary classification. I create two dimensions out of this for visualization with PCA. In this visualization I can see, that data is not separable but ...
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### How to improve de-noise algorithm on low signal-to-noise ratio features?

In the following plot, I have features that all have a very small predictive power on y, there is a low signal-to-noise ratio. In order to de-noise them, I tried ...
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### Unable to meet desired mean squared error

I wish to get MSE < 0.5 on test data (https://easyupload.io/zr7xf3) which is 20% of given data chosen randomly. But I am reaching 0.73 using both plain Ridge Regression as well as a neural network ...