4 ms·
In general these are referred to as embedding techniques PCA/SVD is still common along with other linear methods like ICA, CCA, PLS t-sne is pretty popular stat
by leecarraher 5y ago
In general these are referred to as embedding techniques
PCA/SVD is still common along with other linear methods like ICA, CCA, PLS
t-sne is pretty popular statistically inspired with some manifold learning
non-negative matrix factorization is useful recommender systems
topologically inspired techniques are gaining traction, like UMAP, LLE
the author notes neural embeddings, like autoencoders.
- PaulHoule 5y agoAround the time that this article was written I was putting the finishing touches on a neural autoencoder that was part of a search engine for patents. (The training was done with assembly language SIMD instructions so we weren't in a hurry to make any changes in the network, training algorithms, ...) The search engine worked amazingly well. When we first demoed it we heard from the USPTO in the first week, they wanted to buy a license...
- sydthrowaway 5y agoSo what do you work on now if you were that ahead?