Month: October 2017

William Vorhies describes what unsupervised decision trees are:

In anomaly detection we are attempting to identify items or events that don’t match the expected pattern in the data set and are by definition rare.  The traditional ‘signature based’ approach widely used in intrusion detection systems creates training data that can be used in normal supervised techniques.  When an attack is detected the associated traffic pattern is recorded and marked and classified as an intrusion by humans.  That data then combined with normal data creates the supervised training set.

In both supervised and unsupervised cases decision trees, now in the form of random forests are the weapon of choice.  Decision trees are nonparametric; they don’t make an assumption about the distribution of the data.  They’re great at combining numeric and categoricals, and handle missing data like a champ.  All types of anomaly data tend to be highly dimensional and decision trees can take it all in and offer a reasonably clear guide for pruning back to just what’s important.

To be complete, there is also category of Semi-Supervised anomaly detection in which the training data consists only of normal transactions without any anomalies.  This is also known as ‘One Class Classification’ and uses one class SVMs or autoencoders in a slightly different way not discussed here.

Interesting reading.  I’d had no idea that unsupervised decision trees were even a thing.

Chris Moody wants you to stop using word2vec:

When I started playing with word2vec four years ago I needed (and luckily had) tons of supercomputer time. But because of advances in our understanding of word2vec, computing word vectors now takes fifteen minutes on a single run-of-the-mill computer with standard numerical libraries. Word vectors are awesome but you don’t need a neural network – and definitely don’t need deep learning – to find them. So if you’re using word vectors and aren’t gunning for state of the art or a paper publication then stop using word2vec.

Chris has a follow-up post on word tensors as well:

There’s only three steps to computing word tensors. Counting word-word-document skipgrams, normalizing those counts to form the PMI-like M tensor and then factorizing M into smaller matrices.

But to actually perform the factorization we’ll need to generalize the SVD to higher rank tensors ¹. Unfortunately, tensor algebra libraries aren’t very common ². We’ve written one for non-negative sparse tensor factorization, but because the PMI can be both positive and negative it isn’t applicable here. Instead, for this application I’d recommend HOSVD as implemented in scikit-tensor. I’ve also heard good things about tensorly.

I’m going to keep using word2vec for now, but it’s a good pair of posts.