49 citations · 408 across the 42 of their papers we have counts for
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cs.LG2012★ 2 cited
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations
Yaoliang Yu, James Neufeld, Ryan Kiros +2
We demonstrate that almost all non-parametric dimensionality reduction methods can be expressed by a simple procedure: regularized loss minimization plus singular value truncation.…
cs.LG2012★ 49 cited
Analysis of Kernel Mean Matching under Covariate Shift
Yaoliang Yu, Csaba Szepesvari
In real supervised learning scenarios, it is not uncommon that the training and test sample follow different probability distributions, thus rendering the necessity to correct the…
cs.LG2012★ 16 cited
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering
Yao-Liang Yu, Dale Schuurmans
When data is sampled from an unknown subspace, principal component analysis (PCA) provides an effective way to estimate the subspace and hence reduce the dimension of the data. At…