Decomposing the Human Palate with Matrix Factorization
jeremymcohen.net
Decomposing the Human Palate with Matrix Factorization
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Re: Decomposing the Human Palate with Matrix Factorization
#2If I'm not mistaken, this means that the idea of an "eigentaste" is probably half actual taste (sweet, savoury, sour, fishy) and half a function of local and global society (cooked on a BBQ, cooked in a slow-cooker, considered "Indian").
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.
Re: Decomposing the Human Palate with Matrix Factorization
#3educational and funny, great intro to matrix factorization. Thanks!
Re: Decomposing the Human Palate with Matrix Factorization
#4> In other words, matrix factorization is the real deal. It’s the stuff that separates true data scientists from charlatans — the data alchemists, data phrenologists, and data astrologers of the world
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.
Re: Decomposing the Human Palate with Matrix Factorization
#5> In other words, matrix factorization is the real deal. It’s the stuff that separates true data scientists from charlatans — the data alchemists, data phrenologists, and data astrologers of the world 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…
I have a running joke with my machine learning friends that I will write a Data Science/ML book titled "A Thousand Ways to Say 'Singular Value Decomposition'". The number of papers and techniques out there that are SVD with a few minor tweaks and a unique philosophical interpretation of SVD is hilarious.
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.
Re: Decomposing the Human Palate with Matrix Factorization
#6> In other words, matrix factorization is the real deal. It’s the stuff that separates true data scientists from charlatans — the data alchemists, data phrenologists, and data astrologers of the world 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…
I think an important takeaway here is that when you know very little about the nature of your data, dimensionality reduction is a great place to start. You're right to criticize matrix factorization as a end-all be-all tool for "machine learners", but don't mistake Jeremy's point here -- matrix factorization, and dimensionality reduction techniques in general, is a great first step in understanding any dataset.
Re: Decomposing the Human Palate with Matrix Factorization
#7> In other words, matrix factorization is the real deal. It’s the stuff that separates true data scientists from charlatans — the data alchemists, data phrenologists, and data astrologers of the world 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…
I have a running joke with my machine learning friends that I will write a Data Science/ML book titled "A Thousand Ways to Say 'Singular Value Decomposition'". The number of papers and techniques out there that are SVD with a few minor tweaks and a unique philosophical interpretation of SVD is hilarious. Here are some examples: Principal Component Analysis - SVD does dimensionality reduction where some n% of variance…
> One layer Autoencoder - SVD done by a neural network
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.
Re: Decomposing the Human Palate with Matrix Factorization
#8Where did 40 factors come from? That seems to be entirely arbitrary - I'm not seeing any of the usual factorization diagnostics like scree plots or fits.
Re: Decomposing the Human Palate with Matrix Factorization
#9I find this deeply depressing: "the most heavily studied problem in computer science: how to get people to buy more things".
Re: Decomposing the Human Palate with Matrix Factorization
#10Where did 40 factors come from? That seems to be entirely arbitrary - I'm not seeing any of the usual factorization diagnostics like scree plots or fits.
Right, the choice of 40 as the number of factors was totally arbitrary and unscientific. We tried factorizations with 30, 40, and 50 factors, and chose the one that produced the most interpretable latent factors.