Semi-Supervised Learning with Multi-View Embedding: Theory and Application with Convolutional Neural Networks

By Rie Johnson, T. Zhang. 2015

6 citations recorded by the source corpus.

This paper presents a theoretical analysis of multi-view embedding -- feature embedding that can be learned from unlabeled data through the task of predicting one view from another. We prove its usefulness in supervised learning under certain conditions. The result explains the effectiveness of some existing methods such as word embedding. Based on this theory, we propose a new semi-supervised learning framework that learns a multi-view embedding of small text regions with convolutional neural networks. The method derived from this framework outperforms state-of-the-art methods on sentiment classification and topic categorization.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Thomas Demeester
2. Bart Dhoedt
3. Steven Van Canneyt
4. Cedric De Boom

Citations in the corpus

• Representation learning for very short texts using weighted word embedding aggregation (2016, methodology): “One possibility is to use an multilayer perceptron (MLP) with the whole sentence as an input, or a 1D convolutional neural network (Collobert et al. (2011); Hu et al. (2014); Xu et al. (2015); Johnson and Zhang (2015)).”