This type of SVD keeps only a few significant singular values by zeroing out lower singular values in the Sigma matrix. It is used to reduce noise, data compression, summarization, and dimensionality reduction.
This type of SVD keeps only a few significant singular values by zeroing out lower singular values in the Sigma matrix. It is used to reduce noise, data compression, summarization, and dimensionality reduction.