We study a generalized framework for structured sparsity. It extends the well known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimization problem. This framework provides a straightforward way of favouring prescribed sparsity patterns, such as orderings, contiguous regions and overlapping groups, among others. Available optimi...

Aiming at the deficiency of supervise information in the process of sparse reconstruction in Sparsity Preserving Projections (SPP), a semi-supervised dimensionality reduction method named Constraint-based Sparsity Preserving Projections (CSPP) is proposed. CSPP attempts to make use of supervision information of must-link constraints and cannot-link constraints to adjust the sparse reconstructiv...

Dynamic contrast enhanced MRI requires high spatial resolution for morphological information and high temporal resolution for contrast pharmacokinetics. The current techniques usually have to compromise the spatial information for the required temporal resolution. This paper presents a novel method that effectively integrates sparse sampling, parallel imaging, partial separable (PS) model, and ...

In this paper, we are dealing with a novel data-driven learning method (SparseFIS) for Takagi-Sugeno fuzzy systems, extended by including rule weights. Our learning method consists of three phases: the first phase conducts a clustering process in the input/output feature space with iterative vector quantization. Hereby, the number of clusters = rules is pre-defined and denotes a kind of upper b...

Nonnegative Matrix Factorization (NMF) is a standard tool for data analysis. An important variant is the Sparse NMF problem. A natural measure of sparsity is the L0 norm, however its optimization is NP-hard. Here, we consider a sparsity measure linear in the ratio of the L1 and L2 norms, and propose an efficient algorithm to handle the norm constraints which arise when optimizing this measure. ...

This work proposes an expectation-maximization approach to channel identification and equalization in OFDM. The algorithm exploits the natural constraints imposed by the channel (sparsity, maximum delay spread, and a priori statistical information) and those imposed by the transmitter (pilots, cyclic prefix, and the finite alphabet constraint). These constraints are used to reduce the number of...

Linear factor models with non-negativity constraints have received a great deal of interest in a number of problem domains. In existing approaches, positivity has often been associated with sparsity. In this paper we argue that sparsity of the factors is not always a desirable option, but certainly a technical limitation of the currently existing solutions. We then reformulate the problem in or...

A resource exchange network is considered, where exchanges among nodes are based on reciprocity. Peers receive from the network an amount of resources commensurate with their contribution. We assume the network is fully connected, and impose sparsity constraints on peer interactions. Finding the sparsest exchanges that achieve a desired level of reciprocity is in general NP-hard. To capture nea...

Considering a large-scale energy-harvesting wireless sensor network (EH-WSN) measuring compressible data, sparse random projections are feasible for data well-approximation, and the sparsity of random projections impacts the mean square error (MSE) as well as the system delay. In this paper, we propose an adaptive algorithm for sparse random projections in order to achieve a better tradeoff bet...