In this paper, we propose a robust method for semisupervised training of deep neural
networks for multi-label image classification. To this end, we use ramp loss, which is more …

Data features and class probabilities are two main perspectives when, eg, evaluating model
results and identifying problematic items. Class probabilities represent the likelihood that …

This paper introduces a graph-based semi-supervised embedding method as well as its
kernelized version for generic classification and recognition tasks. The aim is to combine the …

To complete the challenging task of high-dimensional data classification with limited labeled
samples, we propose two semi-supervised learning models, namely the random subspace …

Graph-based embedding methods are very useful for reducing the dimension of high-
dimensional data and for extracting their relevant features. In this paper, we introduce a …

Recently, several inductive and flexible nonlinear data projection methods for graph-based
semi-supervised learning were proposed. These state-of-the art techniques have a good …

摘要半监督学**研究如何同时利用有类标签的样本和无类标签的样例改进学**性能,
成为**年来机器学**领域的研究热点. 鉴于半监督学**的理论意义和实际应用价值 …

Multi-label classification has attracted an increasing amount of attention in recent years. To
this end, many algorithms have been developed to classify multi-label data in an effective …

Graph-based embedding aims to reduce the dimension of high dimensional data and to
extract relevant features for learning tasks. In this letter, we propose an Elastic graph-based …

In this letter, a semisupervised block-sparse graph is proposed for discriminant analysis of
hyperspectral imagery. To overcome the difficulty of not having enough training samples in …