Semi Supervised Learning with Bidirectional Adaptive Pairwise Encoding
In contrast to classic supervised learning methods that demand pre defined class labels, pairwise encoding or side information encoding merely requires pairwise similarity information to drive feature learning, which makes it very appealing for many fundamental tasks such as dimensionality reduction and semi supervised learning. In this paper, we present a novel bimarginal pairwise encoding model, along with deep autoencoder, to learn nonlinear embedding for the aforementioned tasks. The new method learns powerful features that preserve critical pairwise information in a semi supervised manner. It has achieved better performance on the well known yet hard to make improvement benchmark MINIST compared with other methods in the same category, i.e. Autoencoder [4] Invariant Mapping for Dimensionality Reduction [1] Neighborhood Component Analysis [3] and Fixed Bi Margin Pairwise Encoding [11]
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)