Neighborhood Preserving Embedding (NPE) is an un-supervised dimensionality reduction technique. Hence, it is lacking of discriminative capability. Zeng and Luo have proposed Supervised Neighborhood Preserving Embedding (SNPE), which uses class infor-mation of training samples to better describe data intrinsic structure. The robustness of SNPE has been demonstrated since it yields promis-ing recognition results. However, there is no theoretical analysis to explain the good performance. Here, we show analytically that the neighborhood discriminant criterion, which manifested in the objective function of SNPE, is close resembled to Fisher discriminant criterion. SNPE is evaluated in ORL and PIE face databases. The inclusion of class information in data learning results superior performance of SNPE to NPE.
Bibliographical noteFunding Information:
V.L. Murthy and R.V. Shah are supported in part by grants from the National Institutes of Health and the American Heart Association. V.L. Murthy is supported in part by the Melvyn Rubenfire Endowed Professorship.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Electrical and Electronic Engineering