Semi-supervised online kernel semantic embedding for multi-label annotation

Abstract

This paper presents a multi-label annotation method that uses a semantic embedding strategy based on kernel matrix factorization. The proposed method called Semi-supervised Online Kernel Semantic Embedding (SS-OKSE) learns to predict the labels of a document by building a semantic representation of the document features that takes into account the labels, when available. A remarkable characteristic of the algorithm is that it is based on a kernel formulation that allows to model non-linear relationships. The SS-OKSE method was evaluated under a semi-supervised learning setup for a multi-label annotation task, over two text document datasets and was compared against several supervised and semi-supervised methods. Experimental results show that SS-OKSE exhibits a significant improvement, showing that a better modeling can be achieved with an adequate selection/construction of a kernel input representation.