Constrained discriminant neighborhood embedding for high dimensional data feature extraction

Abstract

When handling pattern classification problem such as face recognition and digital handwriting identification, image data is always represented to high dimensional vectors, from which discriminant features are extracted using dimensionality reduction methods. So in this paper, we present a supervised manifold learning based dimensionality reduction method named constrained discirminant neighborhood embedding (CDNE). In the proposed CDNE, on one hand, the class information of samples is taken into account to construct both an inter-class graph and an intra-class graph, where neighborhood points in the intra-class graph are selected from those with the same class and any point in the inter-class graph should sample those labeled different classes as its neighborhood points. On the other hand, locally least linear reconstruction technique is also introduced to model an objective function under the local uncorrelation constraint to explore a discriminant subspace. Compared to some related and state-of-the-art dimensionality reduction methods such as discriminant neighborhood embedding (DNE), supervised locality discriminant manifold learning (SLDML), discriminant sparse neighborhood preserving embedding (DSNPE), local graph embedding based on maximum margin criterion (LGE/MMC), uncorrelated discriminant locality preserving projection (UDLPP) and locally uncorrelated discriminant projection (LUDP), the proposed CDNE has been validated to be efficient and feasible by experimental results on some benchmark face data sets including CMU PIE, ORL and FERET.