where the coefficients are Legendre symbols, is called the p-th Fekete polynomial. In this paper the size of the Fekete polynomials on subarcs is studied. We prove essentially sharp bounds for the average value of |fp(z)| , 0 < q < ∞, on subarcs of the unit circle even in the cases when the subarc is rather small. Our upper bounds are matching with the lower bounds proved in a preceding paper f...

Most traditional variable selection criteria, such as the AIC and the BIC, are (or are asymptotically equivalent to) the penalized likelihood with the L0 penalty, namely, pλ(|β|) = 2λI (|β| = 0), and with appropriate values of λ (Fan and Li [7]). In general, the optimization of the L0-penalized likelihood function via exhaustive search over all subset models is an NP-hard computational problem....

The problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although good results achieved by prior image constrained compressed sensing (PICCS) method, there may be some unsati...

Sparse nonnegative matrix factorization (NMF) is exploited to solve spectral unmixing. Firstly, a novel model of sparse NMF is proposed, where the smoothed L0 norm is used to control the sparseness of the factors corresponding to the abundances. Thus, one need not set the degree of the sparseness in prior any more. Then, a gradient based algorithm NMF-SL0 is utilized to solve the proposed model...

This paper describes a novel technique for promoting sparsity in the modified filtered-x algorithms required for active noise control. The proposed algorithms are based on recent techniques incorporating approximations to the l0-norm in the cost functions that are used to derive adaptive filtering algorithms. In particular, zero-attracting and reweighted zeroattracting filtered-x adaptive algor...

Principal Component Analysis (PCA; Pearson, 1901) is a widely used method for data compression. The goal is to find the best low rank approximation of a given matrix, as judged by minimization of the `2 norm of the difference between the original matrix and the low rank approximation. However, the classical method is not resistant to corruption of individual input data points. Recently, a robus...

Clustered L0 buffers are an interesting alternative to reduce energy consumption in the instruction memory hierarchy of embedded VLIW processors. Currently, the synthesis of L0 clusters is performed as an hardware optimization, where the compiler generates a schedule and based on the given schedule L0 clusters are generated. Since, the result of the clustering depends on the given schedule, it ...

The achievable and converse regions for sparse representation of white Gaussian noise based on an overcomplete dictionary are derived in the limit of large systems. Furthermore, the marginal distribution of such sparse representations is also inferred. The results are obtained via the Replica method which stems from statistical mechanics. A direct outcome of these results is the introduction of...

Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l 0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-comp...