PCA-Based Out-of-Sample Extension for Dimensionality Reduction

Understand

Dimensionality reduction methods are very common in the field of high dimensional data analysis.

• Typically, algorithms for dimensionality reduction are computationally expensive.
• Therefore, their applications for the analysis of massive amounts of data are impractical.
• For example, repeated computations due to accumulated data are computationally prohibitive.