Sparse models: theory and applications

Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels ...

Widely used in social work, grounded theory is one of the oldest and best-known qualitative research methods. Even so, it is often misunderstood. Created at a time when positivism ...

Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of...

Abstract Multiple measurement signals are commonly collected in practical applications, and joint sparse optimization adopts the synchronous effect within multiple m...

Solomon’s article and Binmore’s response exemplify a standard exchange between the game theorist and those critical of applying game theory to ethics. The critic of game theory lis...

Abstract Sparse Polynomial Chaos Expansion(PCE) is widely used in various engineering fields to quantitatively analyse the influence of uncertainty, while alleviate the pro...

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused o...

Group-based sparse representation (GSR) uses image nonlocal self-similarity (NSS) prior to grouping similar image patches, and then performs sparse representation. However, the tra...