Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Calcium imaging is a powerful method to record the activity of neural populations in many
species, but inferring spike times from calcium signals is a challenging problem. We …

This paper showcases the theoretical and numerical performance of the Sliding Frank–
Wolfe, which is a novel optimization algorithm to solve the BLASSO sparse spikes super …

Sparse approximation addresses the problem of approximately fitting a linear model with a
solution having as few non-zero components as possible. While most sparse estimation …

Bird's call often contains distinctive information for discriminating different species. Previous
studies have investigated various features for bird sound classification. This paper proposes …

Abstract Information Pursuit (IP) is a classical active testing algorithm for predicting an output
by sequentially and greedily querying the input in order of information gain. However, IP is …

Sparse recovery aims to reconstruct sparse signals from compressed linear measurements.
In this paper, we propose a sparse recovery algorithm called multiple orthogonal least …

Support recovery of sparse signals from compressed linear measurements is a fundamental
problem in compressed sensing (CS). In this article, we study the orthogonal matching …

In this paper, we analyze the performance guarantee of the orthogonal least squares (OLS)
algorithm expressed in terms of the restricted isometry property (RIP). We show the …

Orthogonal least squares (OLS)-type algorithms are efficient in reconstructing sparse
signals, which include the well-known OLS, multiple OLS (MOLS) and block OLS (BOLS). In …