Dimension reduction methods can be compared based on the type of data structure they preserve:

* **Principal Component Analysis (PCA)**: Focuses on preserving global structure by identifying the axes that account for the largest variance in the data.
* **t-Distributed Stochastic Neighbor Embedding (t-SNE)**: Focuses on preserving local structure (neighborhoods) by mapping high-dimensional data to a lower-dimensional space while maintaining relative distances between nearby points.

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Process of reducing the number of random variables under consideration by obtaining a set of principal variables. Dimension reduction methods transform the variables before fitting a least squares model. The two types of dimension reduction are
1. Feature selection, 
which selects a subset of variables from the original dataset, making regression or classification more accurate.
2. Feature extraction, 
which combines variables into features, reducing the amount of data processed while maintaining the accuracy of the original dataset. 

Dimension Reduction

Principal component analysis (PCA) is a method of unsupervised learning techniques that functions as a method for dimension reduction in regression models.  

As explained in the textbook, the objective of PCA is *to find a low-dimensional representation of the observations that explain a good fraction of the variance* (ISLR).


Principal Components Analysis (PCA)

Excluding irrelevant variables from a multiple regression model. Can be done automatically or manually. 
Important because irrelevant variables decrease the accuracy of your data.

Feature Selection

Reducing an initial set of raw data down to smaller groups so that they are more managable. Usually used when looking at large data sets with multiple variables. 

Feature Extraction

- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Generalized Discriminant Analysis (GDA)
- Singular Value Decomposition (SVD)

Methods of dimensionality reduction

I found this video really helpful when first learning about PCA!
[www.youtube.com/watch?v=jPmV3j1dAv4](https://www.youtube.com/watch?v=jPmV3j1dAv4) 

Helpful video explaining dimensionality reduction/PCA

A typical n × p data matrix X has min(n − 1, p) distinct principal components, but we are not always interested in all of them. Instead, we should strive for the minimum number of principal components required to understand the data properly. This can be done through examining a scree plot, where we can eyeball the scree plot to choose the minimum number of principal components needed to form a good explanation of the variation in the data.

Deciding How Many Principal Components to Use

The principal components represent the directions of the data that captures the maximum variance (in other words, most information). More specifically, principal components are the individual dimensions that are found to be "interesting" through PCA.

What are Principal Components?

The concept of interesting is used to find a small number of dimensions, principal components, that captures the most amount of information where interesting is measured by "the amount that the observations vary along each dimension"(James et al., 389).

Concept of Interesting

Proportion of variance is basically how the total variance, sum of variances of all individual principal components, can be explained by each of the individual principal components with respect to the sum. It is the ratio between the variance of one or more particular principal component(s) and the total variance. With multiple principal components, add up their individual variances and divide by the total variance.

The Proportion of Variance Explained

This article uses a small dataset to show how PCA works steps by steps, it has the same result with sklearn library.
- Step 1: Standardize the dataset.
- Step 2: Calculate the covariance matrix for the features in the dataset.
- Step 3: Calculate the eigenvalues and eigenvectors for the covariance matrix.
- Step 4: Sort eigenvalues and their corresponding eigenvectors.
- Step 5: Pick k eigenvalues and form a matrix of eigenvectors.
- Step 6: Transform the original matrix.


Steps Involved in the PCA

Probabilistic PCA is a dimensionality reduction technique that analyzes data via a lower-dimensional latent space. The PCA probability model is a slightly modified factor analysis model that uses $$\textbf{W}\textbf{W}^{T} + \sigma^{2}\textbf{I}$$ as the covariance of $$\textbf{x}$$, where $$\sigma^{2}$$ is a scalar:

$$ \textbf{x} \sim N(\textbf{x}; \textbf{b}, \textbf{W}\textbf{W}^{T} + \sigma^{2}\textbf{I}) $$

which can be equivalently expressed as:

$$ \textbf{x} = \textbf{W}\textbf{h} + \textbf{b} + \sigma \textbf{z} $$

where $$\textbf{z} \sim N(\textbf{z}; 0, \textbf{I})$$ is noise, $$\textbf{x}$$ is a data vector, $$\textbf{h}$$ is a latent variable, and $$\textbf{W}$$ is a matrix of principal axes relating the latent variables to the data.

Probabilistic PCA

The attached image illustrates what PCA accomplishes on a two-dimensional scatterplot. The first principal component is chosen so that it captures the largest possible amount of variation in the data; here it runs from the bottom-left to the top-right of the plot, aligning with the direction of greatest spread. The second principal component is then set in a direction orthogonal to the first that captures the next most variation, explaining the spread not accounted for by the first component.

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