Hypergraph based Semi-supervised Learning for Gender Classification

Graph-based methods are an important category of semi-supervised learning techniques. However, in many situations the graph representation of relational patterns can lead to substantial loss of information. This is because in real-world problems objects and their features tend to exhibit multiple relationships rather than simple pairwise ones. In this paper, we develop a semi-supervised learning method which is based on a weighted hypergraph representation. There are two main contributions in this paper. The first is that we develop a hypergraph representation based on the attributes of feature vectors, i.e. a feature hypergraph. With this representation, the structural information latent in the data can be more effectively modeled. Secondly, to address semi-supervised classification, we derive a l(1)-norm for the spectral embedding minimization problem on the learned hypergraph. This leads to sparse and direct clustering results. We apply the method to the challenging problem of gender determination using features delivered by principal geodesic analysis (PGA). We obtain a classification accuracy as high as 91% on 2.5D facial needle-maps when 50% of the data are labeled.