feat: add dimensionality reduction algorithms (PCA, LDA, LLE, MDS)

[Sid101098]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

Description

This PR adds implementations of four fundamental dimensionality reduction algorithms:

Algorithms Implemented:

• Locally Linear Embedding (LLE) - Nonlinear manifold learning technique
• Multidimensional Scaling (MDS) - Distance-preserving embedding

Features:

• Complete mathematical implementations with proper documentation
• Comprehensive docstrings with examples for each function
• Type hints and error handling
• Unit tests and doctests for verification
• Helper functions for covariance calculations

Note about multiple algorithms:

This PR contains four related dimensionality reduction algorithms in one file because they share common helper functions and represent a comprehensive dimensionality reduction module. They are closely related and benefit from being implemented together.

This contribution is for Hacktoberfest 2024.