Dictionary learning has became an increasingly important task in machine learning, as it is fundamental to the representation problem. A number of emerging techniques specifically include a codebook learning step, in which a critical knowledge abstraction process is carried out. Existing approaches in dictionary (codebook) learning are either generative (unsupervised e.g. k-means) or discrimina...

In this paper, we suggest to use a modified version of Smoothed0 (SL0) algorithm in the sparse representation step of iterative dictionary learning algorithms. In addition, we use a steepest descent for updating the non unit columnnorm dictionary instead of unit column-norm dictionary. Moreover, to do the dictionary learning task more blindly, we estimate the average number of active atoms in t...

This paper presents a method for sentiment classification, called SESS (SElfSupervised and Syntax-Based method). SESS includes three phases. Firstly, some documents are initially classified based on a sentiment dictionary, and then the sentiments of phrases and documents are iteratively revised. This phase provides some accurately labeled data for the second phase. Secondly, a machine learning ...