Orthogonal Neighborhood Preserving Projections

— Orthogonal Neighborhood Preserving Projections (ONPP) is a linear dimensionality reduction technique which attempts to preserve both the intrinsic neighborhood geometry of the data samples and the global geometry. The proposed technique constructs a weighted data graph where the weights are constructed in a data-driven fashion, similarly to Locally Linear Embedding (LLE). A major difference with the standard LLE where the mapping between the input and the reduced spaces is implicit, is that ONPP employs an explicit linear mapping between the two. As a result, and in contrast with LLE, handling new data samples becomes straightforward, as this amounts to a simple linear transformation. ONPP shares some of the properties of Locality Preserving Projections (LPP). Both ONPP and LPP rely on a k-nearest neighbor graph in order to capture the data topology. However, our algorithm inherits the characteristics of LLE in preserving the structure of local neighborhoods, while LPP aims at pres...

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Effrosini Kokiopoulou, Yousef Saad
• Keywords: Data Mining, Data Samples, ICDM 2005, Linear, Neighborhood Preserving Projections