Title: Sparse Label Smoothing for Semi-supervised Person.
In this paper, we propose an effective approach to semi-supervised classification through kernel-based sparse representation. The new method computes the sparse representation of data in the.
In this paper, we propose a Sparse Semi-Supervised Extreme Learning Machine (S3ELM) via joint sparse regularization for classification, which can automatically prune the model structure via joint sparse regularization technology, to achieve more accurate, efficient and robust classification, when only a small number of labeled training samples are available.
As an emerging method, the semi-supervised extreme learning machine (SSELM), that builds on ELM, has been developed for data classification and shown superiorities in learning efficiency and accuracy.
V. Sindhwani, P. Niyogi, and M. Belkin. A co-regularization approach to semi-supervised learning with multiple views. In Proceedings of the Workshop on Learning with Multiple Views, 22nd ICML, 2005. Google Scholar; S. Sun. Semantic features for multi-view semi-supervised and active learning of text classification.
In this paper, we propose a Sparse Semi-Supervised Extreme Learning Machine (S3ELM) via joint sparse regularization for classification, which can automatically prune the model structure via joint.
In this article we apply the sparse grid approach to semi-supervised classification. We formulate the semi-supervised learning problem by a regularization approach. Here, besides a regression formulation for the labeled data, an additional term is involved which is based on the graph Laplacian for an adjacency graph of all, labeled and unlabeled data points.
Multiview Hessian Discriminative Sparse Coding for Image Annotation 4 regularization (LR) to the sparse coding framework. Gao et al. (8) proposed hypergraph Laplacian regularized sparse coding to preserve the local consistence in the feature space. Although the aforementioned sparse coding algorithms have obtained promising performance.