2 ms·

I appreciate the thought-provoking examples :-) > Humans find and reject "low rank" correlations all the time ... steak and cookies ... Sure, but the appropri

I appreciate the thought-provoking examples :-) > Humans find and reject "low rank" correlations all the time ... steak and cookies ... Sure, but the appropriateness of a low rank approximation depends on what you want to predict. Eg: for predicting basketball success, {height, wingspan} might be a very useful "low rank" description, while for predicting obesity the relevant low rank description might be the body mass index (BMI) or something like it. Wingspan = chest width + 2 * arm length and BMI = weight/height^2 could be composite features "discovered" by such a model+inference toolkit. > Conversely, some doctors have an encyclopedic knowledge of various ailments, and either memorize or infer from experience a particular ailment from a huge collection of possible ailments. If the number of possible ailments is large, their "understanding" is not low rank, it's the opposite. That's exactly why I would not call that understanding, especially if they have to memorize a book full of ailments and symptoms. "Understanding" would entail a (causal) model of underlying physiological problems and the symptoms they give rise to, with a recipe for inferring in reverse. > Low rank does not necessarily mean "understanding", it's just a local minimum in some function of a random variable. I don't understand that statement at all. PS: I don't have any particular affinity to neural networks over other ML models, and don't mean to escalate the hype :-)

nerdponx 8y ago
composite features "discovered" by such a model+inference toolkit. This kind of "feature discovery" is sort of what random forests, gradient boosting, and neural networks already do. You can even do a neutered version of it with full quadratic interactions of all the inputs in a (regularized) linear regression model. For a linear version of this task as an unsupervised problem, you will want to look at PCA. The problem right now is not that we don't have enough unsupervised data analysis techniques, it's that they are hard to extract useful information out of. Interpreting t-SNE, for example, is notoriously difficult. This is why I said advances in the space are necessary. As it stands, you can run all sorts of low dimensional mappings and embeddings on a given data set, and spend an afternoon finding a whole lot of nothing that your business analysts didn't already know.

nerdponx 8y ago

composite features "discovered" by such a model+inference toolkit. This kind of "feature discovery" is sort of what random forests, gradient boosting, and neural networks already do. You can even do a neutered version of it with full quadratic interactions of all the inputs in a (regularized) linear regression model. For a linear version of this task as an unsupervised problem, you will want to look at PCA. The problem right now is not that we don't have enough unsupervised data analysis techniques, it's that they are hard to extract useful information out of. Interpreting t-SNE, for example, is notoriously difficult. This is why I said advances in the space are necessary. As it stands, you can run all sorts of low dimensional mappings and embeddings on a given data set, and spend an afternoon finding a whole lot of nothing that your business analysts didn't already know.