Abstract：Locality Preserving Projections (LPP) aims to preserve local structure of the data by constructing a nearest-neighbor graph. However,it is confronted with the difficulty of parameter selection in the process of graph construction. To solve this problem,an algorithm called parameter-free locality preserving projections (PLPP) is proposed. Firstly,a parameter-free graph construction strategy is designed,which can actively determine neighbors of each data point and assign corresponding edge weights. Then,with the proposed graph construction strategy,PLPP seeks an optimal transformation matrix to preserve local structure of the data in the low dimensional space. Since PLPP needs no parameters in graph construction and takes cosine distance as the similarity weight,it is more efficient and robust to outliers than LPP. Moreover,supervised PLPP (SPLPP) is proposed to improve the discriminant ability of PLPP by considering class information of samples. The experimental results on the ORL,FERET and AR face databases validate the effectiveness of PLPP and SPLPP.
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2013, Vol. 26 
