Table of Contents
Why might NMF be more appropriate matrix factorization model than SVD?
SVD is a more ‘insightful’ factorization technique. NMF gives only U and V matrices, but SVD gives a Sigma matrix also along with these two. Sigma gives us insights into the amount of information each eigen vector holds. You should also consider regularization in NMF while you don’t need to worry about it in SVD.
What is the difference between NMF and PCA?
It shows that NMF splits a face into a number of features that one could interpret as “nose”, “eyes” etc, that you can combine to recreate the original image. PCA instead gives you “generic” faces ordered by how well they capture the original one.
What NMF means?
NMF
| Acronym | Definition |
|---|---|
| NMF | Nigerian Military Force |
| NMF | New Minor Forcing (bridge game convention) |
| NMF | Neuroscience Mutagenesis Facility (Jackson Laboratory; Bar Harbor, ME) |
| NMF | Natural Metal Finish |
How is SVD used in machine learning?
The singular value decomposition (SVD) provides another way to factorize a matrix, into singular vectors and singular values. The SVD is used widely both in the calculation of other matrix operations, such as matrix inverse, but also as a data reduction method in machine learning.
What is alternating alternating least square (ALS)?
Alternating Least Square (ALS) is also a matrix factorization algorithm and it runs itself in a parallel fashion. ALS is implemented in Apache Spark ML and built for a larges-scale collaborative filtering problems.
How can I avoid overfitting in matrix factorization?
A common strategy to avoid overfitting is to add regularization terms to the objective function. The objective of matrix factorization is to minimize the error between true rating and predicted rating:
What is alternating least squares (OLS)?
The solution is ultimately given by the Ordinary Least Squares (OLS) formula . Alternating least squares does just that. It is a two-step iterative optimization process. In every iteration it first fixes P and solves for U, and following that it fixes U and solves for P.
How to improve recommender personalization with more latent factors?
A matrix factorization with one latent factor is equivalent to a most popular or top popular recommender (e.g. recommends the items with the most interactions without any personalization). Increasing the number of latent factors will improve personalization, until the number of factors becomes too high, at which point the model starts to overfit.