Scalability

SVD processes large data sets efficiently, recommending appropriate feature numbers for dimensionality reduction.

Singular Value Decomposition (SVD) can process data sets with millions of rows and thousands of attributes. Oracle Machine Learning automatically recommends an appropriate number of features, based on the data, for dimensionality reduction.

SVD has linear scalability with the number of rows and cubic scalability with the number of attributes when a full decomposition is computed. A low-rank decomposition is typically linear with the number of rows and linear with the number of columns. The scalability with the reduced rank depends on how the rank compares to the number of rows and columns. It can be linear when the rank is significantly smaller or cubic when it is on the same scale.