15 - Introduction to Matrix Factorization (and SVD)

PCA, SVD, MDS, spectral clustering, Latent Semantic Indexing, word embeddings

The presence of negative terms makes it challenging to interpret the factors obtained.

To find two matrices, U and V, that approximate the given data matrix.

<p>Singular Value Decomposition</p> Signup and view all the answers

<p>Truncated SVD involves reducing the dimensions of the original matrix by computing only the first k singular values and vectors.</p> Signup and view all the answers

<p>SVD provides the least-squares best approximations of a matrix up to a certain rank.</p> Signup and view all the answers