this article deals with two main issues: firstly, it investigates the status of sanai's poetry in the development of dehkhoda dictionary. to achieve this end, examples cited from sanai's poems by the authors of dehkhoda dictionary have been examined. the study reveals that in quite a good number of cases, sanai's poems have been the exclusive source for the borrowed examples and the dictionary ...

This paper explores the entertainment experience and learning experience in Scrabble. It proposes a new measure from the educational point of view, which we call learning coefficient, based on the balance between the learner’s skill and the challenge in Scrabble. Scrabble variants, generated using different size of board and dictionary, are analyzed with two measures of game refinement and lear...

High-dimensional datasets are well-approximated by low-dimensional structures. Over the past decade, this empirical observation motivated the investigation of detection, measurement, and modeling techniques to exploit these low-dimensional intrinsic structures, yielding numerous implications for high-dimensional statistics, machine learning, and signal processing. Manifold learning (where the l...

We consider the problem of learning overcomplete dictionaries in the context of sparse coding, where each sample selects a sparse subset of dictionary elements. Our method consists of two stages, viz., initial estimation of the dictionary, and a clean-up phase involving estimation of the coefficient matrix, and re-estimation of the dictionary. We prove that our method exactly recovers both the ...

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...

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the ...

In video-sharing websites and surveillance scenarios, there are often a large amount of face videos. This paper proposes a joint dictionary learning and subspace segmentation method for video-based face recognition (VFR). We assume that the face images from one subject video lie in a union of multiple linear subspaces, and there exists a global dictionary to represent these images and segment t...

The JNPDL model is well motivated by the current drawbacks of dictionary learning approaches, while each constraints are also well designed (the novel discriminative graph constraints are proposed, and all constrains are designed to be easily optimized). Aiming to bridge the gap between features and dictionary, we do think the proposed idea of learning a projection for features jointly with the...

A framework for learning optimal dictionaries for simultaneous sparse signal representation and robust class classification is introduced in this paper. This problem for dictionary learning is solved by a class-dependent supervised simultaneous orthogonal matching pursuit, which learns the intra-class structure while increasing the inter-class discrimination, interleaved with an efficient dicti...

Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage, and severely limits the training data that can be used. Very recent...