Exploring Dimensional Reduction in Star Systems and Galaxies

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In summary, galaxies and star systems tend to reduce their movement through the 3rd "Z" dimension due to conservation of angular momentum. This is because they form out of a cloud with fixed angular momentum and will naturally flatten out in the plane normal to their angular momentum. This behavior is not related to entropy and can be described as a result of angular momentum.
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Like entropy tends towards chaos, why do star systems or galaxies tend to reduce their movement through the 3rd "Z" dimension even though all particles can freely move in all 3 dimensions? Flattening out.

Is there some sort of word or phrase which describes this behavior? Is it possible, interacting matter naturally tends to reduce the number of dimensions they are operating in?
 
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It's due to conservation of angular momentum.

Galaxies and star systems form out of a cloud with some fixed angular momentum. They will tend to flatten out in the plane normal to their angular momentum.

It has nothing to do with entropy.
 
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I know it has nothing to do with entropy, but is there some sort of sub-topic word describing this behavior other then simple angular momentum?
 

1. What is dimensional reduction?

Dimensional reduction is a process in which the number of dimensions in a dataset or system is reduced while maintaining as much relevant information as possible. It is often used in data analysis and machine learning to simplify complex datasets and improve computational efficiency.

2. Why is dimensional reduction important?

Dimensional reduction is important because it can improve the performance of machine learning algorithms by reducing the computational complexity and improving the accuracy of predictions. It also helps to visualize high-dimensional data and identify important features or patterns.

3. What are the different methods of dimensional reduction?

There are two main methods of dimensional reduction: feature selection and feature extraction. Feature selection involves selecting a subset of the original features, while feature extraction creates new features by combining or transforming the original ones. Some common techniques include principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE).

4. What are the potential drawbacks of dimensional reduction?

One potential drawback of dimensional reduction is the loss of information. By reducing the number of dimensions, some relevant information may be discarded, leading to a decrease in the performance of certain models. Additionally, some methods of dimensional reduction may be sensitive to outliers or noise in the data, which can result in distorted representations.

5. When should dimensional reduction be used?

Dimensional reduction should be used when dealing with high-dimensional data that may be difficult to visualize or analyze. It is also helpful when dealing with data that is redundant or noisy, as it can improve the performance and interpretability of models. However, it may not be necessary for all datasets and should be carefully evaluated based on the specific goals and requirements of the analysis.

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