Binary Data Embedding Framework for Multiclass Classification

This paper proposes a novel manifold embedding method for the automated processing of large varied datasets. The method is based on binary classification, where the embeddings are constructed so as to determine one or more unique features for each class individually from a given dataset. The proposed method is applied to examples of multiclass classification that are relevant for large-scale data processing for surveillance (e.g., face recognition), where the aim is to augment decision making by reducing extremely large sets of data to a manageable level before displaying the selected subset of data to a human operator. The method consists of two stages: Preprocessing and embedding computation. In the embedding computation, adaptive measures of intraclass and interclass information are proposed, based on the concepts of “friend closeness” and “enemy dispersion.” In addition, an indicator for weighted pairwise constraint is proposed to balance the contributions from different classes to the final optimization, in order to better control the relative positions between the important data samples from either the same class (intraclass) or different classes (interclass). The effectiveness of the proposed method is evaluated through comparison with seven existing techniques for embedding learning, using four established databases of faces, consisting of various poses, lighting conditions, and facial expressions, as well as two standard text datasets. The proposed method performs better than these existing techniques, especially for cases with small sets of training data samples.