How can truncated SVD be implemented in MATLAB for dimensionality reduction and matrix factorization?

Truncated Singular Value Decomposition (SVD) can be implemented in MATLAB for dimensionality reduction and matrix factorization by using the 'svds' function. This function allows you to specify the number of singular values and vectors to keep, effectively reducing the dimensionality of the original matrix. By selecting a smaller number of singular values and vectors, you can approximate the original matrix with a lower-rank approximation, which can be useful for tasks like data compression and noise reduction.