ESA☆: A generic framework for semi-supervised inductive learning

A multiple kernel framework for inductive semi-supervised SVM learning

We investigate the benefit of combining both cluster assumption and manifold assumption underlying most of the semi-supervised algorithms using the flexibility and the efficiency of multiple kernel learning. The multiple kernel version of Transductive SVM (a cluster assumption based approach) is proposed and it is solved based on DC (Difference of Convex functions) programming. Promising result...

A Multi-kernel Framework for Inductive Semi-supervised Learning

We investigate the benefit of combining both cluster assumption and manifold assumption underlying most of the semi-supervised algorithms using the flexibility and the efficiency of multi-kernel learning. The multiple kernel version of Transductive SVM (a cluster assumption based approach) is proposed and it is solved based on DC (Difference of Convex functions) programming. Promising results o...

A Survey on Inductive Semi-supervised Learning

A unified semi-supervised dimensionality reduction framework for manifold learning

We present a general framework of semi-supervised dimensionality reduction for manifold learning which naturally generalizes existing supervised and unsupervised learning frameworks which apply the spectral decomposition. Algorithms derived under our framework are able to employ both labeled and unlabeled examples and are able to handle complex problems where data form separate clusters of mani...

A novel semi-supervised learning framework for hyperspectral image classification

In this paper, we propose a novel semi-supervised learning classification framework using box-based smooth ordering and Multiple 1D-embedding-based interpolation method in Ref. 25 for hyperspectral images. Due to the lack of labeled samples, conventional supervised approaches cannot generally perform efficient enough. On the other hand, obtaining labeled samples for hyperspectral image classifi...