Abstract：Locality Preserving Projection (LPP) is a typical method of neighbor
graph based dimensionality reduction algorithm. So, graph
construction plays a key role on the performance of LPP. The
original samples were transformed into their vectorial form by the
traditional graph construction method before calculate k-nearest
neighbors of each samples, which will lost Sample's inner structure
information. In this paper, we proposed a new graph construction
approach which called Corresponding Block (CB) Based Neighbor Graph
Construction Method, and we named the so constructed graph as
Corresponding Block Based Graph (CBG). Our new method divided each
sample matrix into several blocks and base on corresponding blocks
to determine neighbors of each sample, which can well preserve
samples' intrinsic structural information and has the ability of
non-uniform illumination immunity in some extent. Then, we
incorporate CBG into the state-of-art dimensionality algorithm: LPP,
and developed a new algorithm called CBG-LPP. To evaluate CBG-LPP,
several experiments were conducted on three well-known face
databases and achieved satisfactory results.
Bin Li;Anan Du;Yecheng Zhang;Xiangchun Yu;Zhezhou Yu. Corresponding Block Based Graph Construction for Locality Preserving Projection[J]. , 2014, 11(11): 3967-3974.
Bin Li;Anan Du;Yecheng Zhang;Xiangchun Yu;Zhezhou Yu. Corresponding Block Based Graph Construction for Locality Preserving Projection. , 2014, 11(11): 3967-3974.
2014, Vol. 11 
