Mini toolsDefinition
A dimensionality reduction technique that transforms data into a new coordinate system of uncorrelated variables.
Detailed Explanation
A statistical method that converts correlated features into linearly uncorrelated components by finding principal components (orthogonal directions of maximum variance) in the data. It performs eigendecomposition of the covariance matrix to identify these components. Each component is orthogonal to the others, making it useful for data compression, visualization, feature extraction, and noise reduction.
Use Cases
1. Data visualization 2. Feature extraction 3. Image compression 4. Noise reduction
