Questions tagged [dimensionality-reduction]

For questions related to AI methods of dimensionality reduction (e.g. PCA or autoencoders).

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How to cluster data points such that the number of clusters is kept minimal and each cluster projects well onto a lower-dimensional subspace?

If I want to find a (linear) subspace onto which a data-set projects well, I can simply use PCA. However, often the data can project with much smaller error if I first separate it into a couple of ...
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How to deal with large number of features for Anomaly Detection

I am trying to build anomaly detection with low false positives .Dataset that i am using is a patient health sensor data. A number of parameters from the patient's sensors are collected hourly and I ...
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28 views

PCA + LDA feature extraction

I am trying to reduce the size of my features vectors using PCA and LDA. Following the approach presented here, I cannot understand step 3 and step 4 described in that approach. Why is the ...
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41 views

How can I use autoencoders to analyze patterns and classify them?

I generated a bunch of simulation data from a complex physical simulation that spits out patterns. I am trying to apply unsupervised learning to analyze the patterns and ideally classify them into ...
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2answers
62 views

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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Clustering of very high dimensional data and large number of examples without losing info in dimensions

I'm trying to get a grasp on scalability of clustering algorithms, and have a toy example in mind. Let's say I have around a million or so songs from $50$ genres. Each song has characteristics - some ...
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2k views

What are the purposes of autoencoders?

Autoencoders are neural networks that learn a compressed representation of the input in order to later reconstruct it, so they can be used for dimensionality reduction. They are composed of an encoder ...