I certainly hope this is sarcasm. Matrix factorization is, like, the go-to tool for people that don't know anything about what they are studying (i.e. it's the phrenologist's favorite weapon). Factor a matrix, throw the results out there, slap on some perfunctory "discussion" that has no real mechanistic insight. Boom. Published.
But maybe I'm describing "the stuff that separates true scientists from data scientists".
Data science manifesto: The purpose of computing is numbers.
Here are some examples:
Principal Component Analysis - SVD does dimensionality reduction where some n% of variance should be accounted for.
One layer Autoencoder - SVD done by a neural network
Latent Semantic Analysis - SVD on td-idf matrix we interrupt lower dimensions as having semantic importance
Matrix Factorization - SVD only now we interrupt lower dimensions as representing latent variables
Collaborative Filtering - SVD where we interrupt lower dimensions as representing latent variables AND we use a a distance measure to determine similarity.
Not necessarily. Any serious user of autoencoders would apply some kind of L1 regularization or other sparsity constraint to the coefficients learned, so that the autoencoder does not learn the principal components of the data but instead learns an analogous sparse decomposition of the data (with the assumption that sparse representations have better generalization power).
Also I don't think any of the techniques you mentioned is being passed as "not SVD" by its practitioners. People know they're SVD. These names are just used as labels for use cases of SVD, each with their specific (and crucial) bells and whistles. And yes, these labels are useful.
Cognition is fundamentally dimensionality reduction over a space of information, so clearly most ML algorithms are going to be isomorphic to SVD in some way. More interesting to me are the really non-obvious ways in which that is happening (eg. RNNs learning word embeddings with skip-gram are actually factorizing a matrix of pairwise mutual information of words over a local context...)
That doesn't make these algorithms any less valuable.
That would probably be the second thing people learn right after learning how to do some basic regression analysis.
Still, great walk through, it was frank and I loved it!
It makes sense. You can change the desirability of the same pizza by either 1) cooking it on the BBQ, or 2) calling it a vegetarian pizza, even if the end product would be virtually identical in the end.
Obviously this is reflective of the subset of society that gets recipes from AllRecipes.com, but I'll wager that you'd find similar categories if you did a larger analysis.
[1] www.Gastrograph.com [2] JasonCEC [at] ^above url
And a very interesting application of NNMF, didn't know it took so long to process the data (but then again, Matlab is usually slow)
