Two-Dimensional Neighborhood Discriminant Projection

Classical linear dimensional reduction algorithms, such as Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) have been widely used in computer vision and pattern recognition. However, when dealing with the multidimensional dataset, they usually first transform the original data to vectors, and then analyze the data in such a high dimensional space. This process inevitably results in some obvious disadvantages. This paper proposes a novel two-dimensional dimensionality reduction algorithm called 2D Neighborhood Discriminant Projection (2D-NDP), which is based directly on 2D image matrices rather than ID vectors. 2D-NDP detects the intrinsic class-relationships between the images by incorporating both class label information and neighborhood information. It can optimally preserve not only the local class information but discriminant information as well. Under the orthogonal constrain, 2D-NDP is developed as orthogonal 2D-NDP for classification. Experiments on the face database and the plant leaf database demonstrate that orthogonal 2D-NDP is effective and feasible for classification.