Due to the dramatic expanse of data categories and the lack of labeled instances, zero-shot learning, which transfers knowledge from observed classes to recognize unseen classes, has started drawing a lot of attention from the research community. In this paper, we propose a semi-supervised max-margin learning framework that integrates the semisupervised classification problem over observed clas...

The Locally Linear Embedding (LLE) algorithm is an unsupervised nonlinear dimensionality-reduction method, which reports a low recognition rate in classification because it gives no consideration to the label information of sample distribution. In this paper, a classification method of supervised LLE (SLLE) based on Linear Discriminant Analysis (LDA) is proposed. First, samples are classified a...

In this paper we propose a new watermarking technique where the watermark is embedded according to two keys. The first key is used to embed a code bit in a block of pixels. The second is used to generate the whole sequence of code bits. The watermark is embedded in spatial domain by adding or subtracting a random digital pattern to the given image signal. The embedding depth level depends on th...

This paper proposes a novel graph based learning approach to classify agricultural datasets, in which both labeled and unlabelled data are applied to the classification procedure. In order to capture the complex distribution of data, we propose a similarity refinement approach to improve the robustness of traditional label propagation. Then the refined affinity matrix is applied to label propag...

A Bayesian kernel-based clustering method is presented. The associated model arises as an embedding of the Potts density for label membership probabilities into an extended Bayesian model for joint data and label membership probabilities. The method may be seen as a principled extension of the so-called super-paramagnetic clustering. The model depends on three parameters: the temperature, the k...

Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHESSION, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. ...

As we all know, multi-view data is more expressive than single-view and multi-label annotation enjoys richer supervision information single-label, which makes learning widely applicable for various pattern recognition tasks. In this complex representation problem, three main challenges can be characterized as follows: i) How to learn consistent representations of samples across views? ii) explo...

In view of the limitations current research on students’ single attribute psychological problems. this paper, a multiattribute social network model was constructed based data and label data, improved MANE algorithm used to solve problem predict addition, DeepWalk Node2vec embedding algorithms were embed network, respectively, so verify effectiveness model. Finally, prediction problems, paper us...

Many collective human activities have been shown to exhibit universal patterns. However, the possibility of regularities underlying researcher migration in computer science (CS) has barely been explored at global scale. To a large extend, this is due to official and commercial records being restricted, incompatible between countries, and especially not registered across researchers. We overcome...

background recombinant activated factor vii (rfviia; novoseven® rt, novo nordisk, bagsvaerd, denmark) is a synthetic pro-coagulation factor derived from hamster kidney cells. objectives the purpose of this study was to evaluate the prescribing patterns of recombinant factor viia (rfviia) at a single, tertiary care pediatric hospital by indication of usage and dose administered. materials and me...