Questions tagged [dimensionality-reduction]

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

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How to reduce the dimensionality of the actions in RL

I have a single-agent RL model in which the dimension of the dimension of the action space is $70$. This action space is too big and the deep RL agent is not learning properly. The boundaries of the ...
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Training different ML models on PLS transformed reduced-dimension inputs

I want to perform dimensionality reduction using Partial Least Squares on a complex, large-dimension data set before training various regression models on the reduced-dimension data set. I understand ...
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Perform clustering on high dimensional data

Recently I trained a BYOL model on a set of images to learn an embedding space where similar vectors are close by. The performance was fantastic when I performed approximate K-nearest neighbours ...
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Is it generally advisable to have a low dimensional action space in Reinforcement Learning?

In supervised or unsupervised learning, it is advised to reduce the dimensionality due to the curse of dimensionality in general. Is this also generally advisable for the action space of reinforcement ...
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How do I select the number of neurons for each layer in an auto-encoder for dimensionality reduction?

I am trying to apply an auto-encoder for dimensionality reduction. I wonder how it will be applied on a large dataset. I have tried this code below. I have total of 8 features in my data and I want to ...
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How does t-SNE preserves embedding orders?

According to the triplet loss Wikipedia page: t-SNE (t-distributed Stochastic Neighbor Embedding) preserves embedding orders via probability distributions, whereas triplet loss works directly on ...
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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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Does Linear Discriminant Analysis make dimensionality reduction before classification?

I'm trying to understand what LDA exactly does when used as a classifier. I've understood how the dimensionality reduction works and I've understood that the classification task is carried out with ...
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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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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 ...
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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 ...
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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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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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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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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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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 ...
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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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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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4 answers
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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 ...
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