In this paper, it is proved that dictionary learning and sparse representation is invariant to a linear transformation. It subsumes the special case of transforming/projecting the data into a discriminative space. This is important because recently, supervised dictionary learning algorithms have been proposed, which suggest to include the category information into the learning of dictionary to ...

al-sâmi fi al-asâmi is one of the important and authentic arabic-to-persian dictionaries, and has been used by many of the subsequent lexicographers. in this dictionary the subjects are first classified and then divided to some chapters. the author has gathered some arabic words in each chapter and has given their persian equals. in this article the dialectal and old words of this dictionary ar...

The aim of the article is to elaborate on principles online edition Normative Dictionary Polish Place Names from area Lithuania. This a normative dictionary. It contains equivalents contemporary Lithuanian place names Lithuania, their declination, adjectives and residents. dictionary districts, towns villages, counting over 20 residents Vilnius, Šalčininkai, Trakai, Švenčionys Širvintos regions...

Abstract. Dictionary learning is a challenge topic in many image processing areas. The basic goal is to learn a sparse representation from an overcomplete basis set. Due to combining the advantages of generic multiscale representations with learning based adaptivity, multiscale dictionary representation approaches have the power in capturing structural characteristics of natural images. However...

Recent research has shown that students involved in computer-based second language learning prefer to use a digital dictionary in which a word can be looked up by clicking on it with a mouse (i.e., click-on dictionary) to a digital dictionary in which a word can be looked up by typing it on a keyboard (i.e., key-in dictionary). This study investigated whether digital dictionary format also diff...

A traditional forward dictionary is where the user inputs a word and gets one or more definitions as output. Whereas, in a reverse dictionary the user enters the desired phrase with logic and gets a certain number of words as outputs similar to the concept of the user input phrase. The reverse dictionary addresses the wide spread problem of knowing the meaning of a word, but unable to recall th...

Traditional Compressive Sensing (CS) recovery techniques resorts a dictionary matrix to recover a signal. The success of recovery heavily relies on finding a dictionary matrix in which the signal representation is sparse. Achieving a sparse representation does not only depend on the dictionary matrix, but also depends on the data. It is a challenging issue to find an optimal dictionary to recov...

The dictionary matching problem preprocesses a set of patterns and finds all occurrences of each of the patterns in a text when it is provided. We focus on the dynamic setting, in which patterns can be inserted to and removed from the dictionary, without reprocessing the entire dictionary. This article presents the first algorithm that performs dynamic dictionary matching on two-dimensional dat...

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The s...