1.9k citations · 6.2k across the 55 of their papers we have counts for
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Canonical Correlation Analysis in high dimensions with structured regularization
Elena Tuzhilina, Leonardo Tozzi, Trevor Hastie
Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analys…
Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays
Łukasz Kidziński, Francis K. C. Hui, David I. Warton +1
Unmeasured or latent variables are often the cause of correlations between multivariate measurements, which are studied in a variety of fields such as psychology, ecology, and medi…
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach
Yifang Liu, Zhentao Xu, Qiyuan An +3
Relevance and diversity are both important to the success of recommender systems, as they help users to discover from a large pool of items a compact set of candidates that are not…
Backfitting for large scale crossed random effects regressions
Swarnadip Ghosh, Trevor Hastie, Art B. Owen
Regression models with crossed random effect errors can be very expensive to compute. The cost of both generalized least squares and Gibbs sampling can easily grow as (or…
Feature-weighted elastic net: using "features of features" for better prediction
J. Kenneth Tay, Nima Aghaeepour, Trevor Hastie +1
In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additiona…
Ridge Regularization: an Essential Concept in Data Science
Trevor Hastie
Ridge or more formally regularization shows up in many areas of statistics and machine learning. It is one of those essential devices that any good data scientist needs to…