Truncated SVD

Truncated SVD is a dimensionality reduction technique that projects high-dimensional data onto a lower-dimensional space while preserving as much information as possible. The method, also known as Truncated Singular Value Decomposition, is an alternative to PCA (Principal Component Analysis), which can be sensitive to noise and outliers. Truncated SVD is less sensitive to noise and can handle sparse data, making it a good choice for real-world datasets. The technique is widely used in various applications, including recommendation systems, image and text analysis, and recommender systems. It's also used in the field of neuroscience to analyze brain activity patterns.