Questions tagged [dimensionality-reduction]
For questions related to AI methods of dimensionality reduction (e.g. PCA or autoencoders).
7
questions
1
vote
0answers
53 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 ...
0
votes
0answers
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 ...
0
votes
0answers
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 ...
1
vote
1answer
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 ...
4
votes
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 ...
2
votes
0answers
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 ...
11
votes
4answers
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 ...
