Real-life data seldom comes in the ideal form for statistical learning.
This talk focuses on high-dimensional problems for signals and
discrete entities: when dealing with many, correlated, signals or
entities, it is useful to extract representations that capture these
Matrix factorization models provide simple but powerful representations. They are used for recommender systems across discrete entities such as users and products, or to learn good dictionaries to represent images. However they entail large computing costs on very high-dimensional data, databases with many products or high-resolution images. I will present an
algorithm to factorize huge matrices based on stochastic subsampling that gives up to 10-fold speed-ups .
With discrete entities, the explosion of dimensionality may be due to variations in how a smaller number of categories are represented. Such a problem of "dirty categories" is typical of uncurated data sources. I will discuss how encoding this data based on similarities recovers a useful category structure with no preprocessing. I will show how it interpolates between one-hot encoding and techniques used in character-level natural language processing.
 Stochastic subsampling for factorizing huge matrices, A Mensch, J Mairal, B Thirion, G Varoquaux, IEEE Transactions on Signal Processing 66 (1), 113-128
 Similarity encoding for learning with dirty categorical variables. P Cerda, G Varoquaux, B Kégl Machine Learning (2018): 1-18