Questions tagged [dimensionality-reduction]

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

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Deep Continuous Clustering algorithm - just one output cluster

I use the DCC algorithm to cluster some data. The whole algorithm is available here, but shortly it is: construct mkNN graph of the data points (the connected components of it are the clusters). ...
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1answer
77 views

How do AI researchers imagine higher dimensions?

We can visualize single, two, and three dimensions using websites or imagination. In the context of AI and, in particular, machine learning, AI researchers often have to deal with multi-dimensional ...
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1answer
32 views

How classification neural nets are different from simple dimension reduction + clustering?

I know the training of neural nets involves some sort of dimension manipulation to separate classes of different features. If there is no variation of features, no matter for neural nets or simple ...
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54 views

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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31 views

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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1answer
42 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
65 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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20 views

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 ...