Similarity-balanced Discriminant Neighborhood Embedding
Chuntao Ding, Li Zhang, Yaping Lu, Shuping He · 2014
The idea that with the help of proper dimensionality reduction, trying to make the samples with the same label be compact and the ones with the different labels be separate after projection, is introduced into classification problems with high-dimensional data. Based on the analysis of the drawbacks of Discriminant Neighborhood Embedding (DNE) and Locality-Based Discriminant Neighborhood Embedding (LDNE), being the two relatively successful Locally Discriminant Analysis methods proposed in recent years, this paper proposes a method called Similarity-balanced Discriminant Neighborhood Embedding (SBDNE). When constructing the adjacent graph, SBDNE fully takes into account the geometric construction of manifold and the problem of imbalance between the intra-class points and the inter-class points. By endowing these two kinds of samples with different similarities and selecting the near neighbors according to the similarity matrix, not only the structure in the original space can be preserved more efficiently, but also the choice of discriminative information increases. The method proposed here has a better recognition with comparisons to some classical methods, which fully shows that SBDNE method has the capacity to efficiently solve the classification problem.