Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the presented method outperforms its state-of-the-art competitors and has several adv...

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into...

Conventional dictionary learning algorithms suffer from the following problems when applied to face recognition. First, since in most face recognition applications there are only a limited number of original training samples, it is difficult to obtain a reliable dictionary with a large number of atoms from these samples. Second, because the face images of the same person vary with facial poses ...

Learning based image super-resolution method is used to reconstruct high-frequency (HF) details from the prior model trained by a set of high-(HR) and low-resolution (LR) image patches. In this paper, newly proposed novel image superresolution method via dual-dictionary learning and sparse representation, which consists of the main dictionary learning and the residual dictionary learning, to re...

The objective of this paper is to propose a parallel implementation of dictionary learning based multiperson tracking on single camera. After its success of face recognition, dictionary learning is recently adapted to multi-person tracking and achieves very promising results [2]. However, by recognizing that the computational complexity of dictionary learning is a big factor preventing tracking...

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative diction...

Sparse representation classification (SRC) is being widely investigated on hyperspectral images (HSI). For SRC methods to achieve high classification performance, not only is the development of sparse representation models essential, the designing and learning of quality dictionaries also plays an important role. That is, a redundant dictionary with well-designated atoms is required in order to...

the present study aimed at investigating whether vocabulary-learning strategies had any impact on the vocabulary learning of iranian efl learners. the participants of the study were 67 male and female undergraduate university students, majoring in english translation and english literature at islamic azad university, north tehran branch, who were taking “reading comprehension ii” course. in ord...