Dimensionality Reduction Impact on Machine Learning in Hyperspectral Imaging
Analysis
This research article from ArXiv investigates the impact of Principal Component Analysis (PCA) for dimensionality reduction on machine learning performance in hyperspectral optical imaging. The study likely explores trade-offs between computational efficiency and accuracy when applying PCA.
Key Takeaways
- •Investigates the use of PCA for dimensionality reduction.
- •Focuses on hyperspectral optical imaging.
- •Assesses the impact on machine learning performance.
Reference
“The research focuses on the effect of PCA-based dimensionality reduction.”