Questions tagged [dimensionality-reduction]
For questions related to AI methods of dimensionality reduction (e.g. PCA or autoencoders).
14
questions
2
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1answer
147 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 ...
3
votes
1answer
55 views
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 ...
2
votes
0answers
45 views
Estimating dimensions to reduce input image size to in CNNs
Considering input images to a CNN that have a large dimension (e.g. 256X256), what are some possible methods to estimate the exact dimensions (e.g. 16X16 or 32X32) to which it can be condensed in the ...
2
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0answers
27 views
Compressing Parameters of an Response System
I have an input-output system, which is fully determined by 256 parameters, of which I know a significant amount are of less importance to the input-output pattern.
The data I have is some (64k in ...
3
votes
0answers
18 views
What is meant by subspace clustering in MFA?
The basic idea of MFA is to perform subspace clustering by assuming the covariance structure for each component of the form, $\Sigma_i = \Lambda_i \Lambda_i^T + \Psi_i$, where $\Lambda_i \in \mathbb{R}...
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36 views
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).
...
3
votes
1answer
112 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 ...
1
vote
1answer
35 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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0answers
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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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 ...
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vote
2answers
68 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 ...
5
votes
2answers
75 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
21 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 ...
