Projection filters

Projection filters, in the context of signal processing and machine learning, refer to techniques used to extract specific features or components from signals or data by projecting them into a lower-dimensional space or onto a certain subspace. This can be particularly useful for noise reduction, feature extraction, and dimensionality reduction. Here’s an overview of their main aspects: 1. **Mathematical Basis**: A projection filter typically involves linear algebra concepts, where data is represented as vectors in a high-dimensional space.