Sparse coding, which is represented a vector based on sparse linear combination of a dictionary, is widely applied on signal processing, data mining and neuroscience. How to get a proper dictionary is a problem, which is data dependent and computational cost. In this paper, we treat dictionary learning in the unsupervised learning view and proposed Laplacian score dictionary (LSD). This new met...

Given a hypergraph H with m hyperedges and a set Q of m pinning subspaces, i.e. globally fixed subspaces in Euclidean space R, a pinned subspace-incidence system is the pair (H,Q), with the constraint that each pinning subspace in Q is contained in the subspace spanned by the point realizations in R of vertices of the corresponding hyperedge of H . This paper provides a combinatorial characteri...

INTRODUCTION The dictionary is one of the common learning tools for second and foreign language learners. Various types of dictionaries are used to help learners work on their language development. A bilingual dictionary is often the first dictionary that a foreign language learner encounters. A study conducted on dictionary usage in seven European countries, including over 1,100 learners of En...

Task-Driven Dictionary Learning for HyperspectralImage Classification with Structured SparsityConstraints Report Title Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction error for the signal. Howe...

MOTIVATION From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database iden...

This paper presents a novel dictionary learning method for image denoising, which removes zero-mean independent identically distributed additive noise from a given image. Choosing noisy image itself to train an over-complete dictionary, the dictionary trained by traditional sparse coding methods contains noise information. Through mathematical derivation of equation, we found that a lower bound...

A new dictionary learning method for exact sparse representation is presented in this paper. As the dictionary learning methods often iteratively update the sparse coefficients and dictionary, when the approximation error is small or zero, algorithm convergence will be slow or non-existent. The proposed framework can be used in such a setting by gradually increasing the fidelity of the approxim...

The features of R{j}ecnik.com dictionary, as one of the first on-line English-Serbo-Croatian dictionaries are presented. The dictionary has been on-line for the past five years and has been frequently visited by the Internet users. We evaluate and discuss the system based on the analysis of the collected data about site visits during this five-year period. The dictionary structure is inspired b...

The Crystallographic Information File (CIF) uses the self-defining STAR file structure. This requires the creation of a dictionary of data names and definitions. A basic dictionary of terms needed to describe the crystal structures of small molecules was approved in 1991 and is currently used for the submission of papers to Acta Crystallographica C. A number of extensions to this dictionary are...