Sparse recovery in convex hulls via entropy penalization

نویسندگان

چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Sparse Recovery in Convex Hulls of Infinite Dictionaries

Let S be an arbitrary measurable space, T ⊂ R and (X,Y ) be a random couple in S × T with unknown distribution P. Let (X1, Y1), . . . , (Xn, Yn) be i.i.d. copies of (X,Y ). Denote by Pn the empirical distribution based on the sample (Xi, Yi), i = 1, . . . , n. Let H be a set of uniformly bounded functions on S. Suppose that H is equipped with a σ-algebra and with a finite measure μ. Let D be a ...

متن کامل

Metric Entropy of Convex Hulls

Let T be a precompact subset of a Hilbert space. The metric entropy of the convex hull of T is estimated in terms of the metric entropy of T , when the latter is of order α = 2. The estimate is best possible. Thus, it answers a question left open in [LL] and [CKP]. 0.

متن کامل

Sparse Gaussian Process Regression via L1 Penalization

To handle massive data, a variety of sparse Gaussian Process (GP) methods have been proposed to reduce the computational cost. Many of them essentially map the large dataset into a small set of basis points. A common approach to learn these basis points is evidence maximization. Nevertheless, evidence maximization may lead to overfitting and cause a high computational cost. In this paper, we pr...

متن کامل

`1-magic : Recovery of Sparse Signals via Convex Programming

For maximum computational efficiency, the solvers for each of the seven problems are implemented separately. They all have the same basic structure, however, with the computational bottleneck being the calculation of the Newton step (this is discussed in detail below). The code can be used in either “small scale” mode, where the system is constructed explicitly and solved exactly, or in “large ...

متن کامل

Recovery of Sparse Probability Measures via Convex Programming

We consider the problem of cardinality penalized optimization of a convex function over the probability simplex with additional convex constraints. The classical `1 regularizer fails to promote sparsity on the probability simplex since `1 norm on the probability simplex is trivially constant. We propose a direct relaxation of the minimum cardinality problem and show that it can be efficiently s...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: The Annals of Statistics

سال: 2009

ISSN: 0090-5364

DOI: 10.1214/08-aos621