Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression

25 Jul 2017Shiva Prasad KasiviswanathanMark Rudelson

High-dimensional settings, where the data dimension ($d$) far exceeds the number of observations ($n$), are common in many statistical and machine learning applications. Methods based on $\ell_1$-relaxation, such as Lasso, are very popular for sparse recovery in these settings... (read more)

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