The Generalized Lasso With Non-Linear Observations
نویسندگان
چکیده
منابع مشابه
on the effect of linear & non-linear texts on students comprehension and recalling
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15 صفحه اولHigh - Dimensional Generalized Linear Models and the Lasso
We consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical risk minimizer with Lasso penalty. The penalty is based on the coefficients in the linear predictor, after normalization with the empirical norm. The examples include logistic regression, density estimation and classification with hinge loss. Least ...
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Consider estimating an unknown, but structured (e.g. sparse, low-rank, etc.), signal x0 ∈ R from a vector y ∈ R of measurements of the form yi = gi(aiTx0), where the ai’s are the rows of a known measurement matrix A, and, g(·) is a (potentially unknown) nonlinear and random link-function. Such measurement functions could arise in applications where the measurement device has nonlinearities and ...
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متن کاملLASSO with Non-linear Measurements is Equivalent to One With Linear Measurements
Consider estimating an unknown, but structured (e.g. sparse, low-rank, etc.), signal x0 ∈ R from a vector y ∈ R of measurements of the form yi = gi(aix0), where the ai’s are the rows of a known measurement matrix A, and, g(·) is a (potentially unknown) nonlinear and random link-function. Such measurement functions could arise in applications where the measurement device has nonlinearities and u...
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ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2016
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2016.2517008