introduction: compulsive internet use scale (cius) is a newest and prestigious questionnaire in the diagnosis of internet addiction .the goals of this study was to investigate the reliability and validity (psychometric properties) of persian version of cius in internet users of isfahan's universities. methods: in this descriptive and cross- sectional research  400 isfahan university students we...

Linear feature transformation technique is widely used to improve feature discriminability. It can reduce the dimensionality of the feature space, un-correlate the feature components, hence more discriminative model can be obtained. In this paper we compare three discriminative linear transformation approaches in Mandarin digit string recognition (MDSR) system. Compared with the conventional Li...

Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to ...

A discriminative feature extraction based on Principal component analysis (PCA) and Gaussian mixture models is presented to increase the discrimative capability of modified two dimentional root cepstrum analysis (MTDRC). The exprimental results show that F-ratio tests indicate better separability of phonemes by using discriminative feature extraction than MTDRC. Key–Words: Feature extraction,PC...

We study discriminative joint density models, that is, generative models for the joint density p(c,x) learned by maximizing a discriminative cost function, the conditional likelihood. We use the framework to derive generative models for generalized linear models, including logistic regression, linear discriminant analysis, and discriminative mixture of unigrams. The benefits of deriving the dis...

This paper proposes learning a non-linear transform with two priors. The first is a discriminative prior defined using a measures on a support intersection and the second is a minimum information loss prior expressed as a constraint on the conditioning and the coherence. An approximation of the measures for the discriminative prior is addressed, connecting it to a similarity concentrations. Alo...

Temporal multi-document summarization (TMDS) aims to capture evolving information of a single topic over time and produce a summary delivering the main information content. This paper presents a cascaded regression analysis based macro-micro importance discriminative model for the content selection of TMDS, which mines the temporal characteristics at different levels of topical detail in order ...

Dimensionality reduction is one of the widely used techniques for data analysis. However, it is often hard to get a demanded low-dimensional representation with only the unlabeled data, especially for the discriminative task. In this paper, we put forward a novel problem of Transferred Dimensionality Reduction, which is to do unsupervised discriminative dimensionality reduction with the help of...