Locality-Based Discriminant Neighborhood Embedding

Experimental results show that the linear supervised subspace learning method called LDNE can be an effective and robust method for classification and compare it with the state-of-the-art dimensionality reduction techniques such as LPP and DNE on publicly available datasets.

Abstract

In this article, we develop a linear supervised subspace learning method called locality-based discriminant neighborhood embedding (LDNE), which can take advantage of the underlying submanifold-based structures of the data for classification. Our LDNE method can simultaneously consider both ‘locality’ of locality preserving projection (LPP) and ‘discrimination’ of discriminant neighborhood embedding (DNE) in manifold learning. It can find an embedding that not only preserveslocalinformationtoexploretheintrinsicsubmanifoldstructureofdatafromthesameclass, but also enhances the discrimination among submanifolds from different classes. To investigate the performance of LDNE, we compare it with the state-of-the-art dimensionality reduction techniques such as LPP and DNE on publicly available datasets. Experimental results show that our LDNE can be an effective and robust method for classification.