Paper Info
Title
Robust Locality Preserving Projections With Cosine-Based Dissimilarity for Linear Dimensionality Reduction.
Abstract
Locality preserving projection (LPP) is a classical tool for dimensionality reduction problems. However, it is sensitive to outliers because of utilizing the 2-norm-based distance criterion. In this paper, we propose a new approach, termed Euler-LPP, by preserving the local structures of data under the distance criterion of the cosine-based dissimilarity. Euler-LPP is robust to outliers in that the cosine-based dissimilarity suppresses the influence of outliers more efficiently than the.e2-norm. An explicit mapping, defined by a complex kernel (euler kernel) is adopted to map the data from the input space to complex reproducing kernel Hilbert spaces (CRKHSs), in which the distance of the data pairs under the,e2-norm is equal to that in the input space under the cosine-based dissimilarity. Thus, the robust dimensionality problem can be directly solved in CRKHS, where the solution is guaranteed to converge to a global minimum. In addition, Euler-LPP is easy to implement without significantly increasing computational complexity. Experiment results on several benchmark databases confirm the effectiveness of the proposed method.
Year
DOI
Venue
2017
10.1109/ACCESS.2016.2616584
IEEE ACCESS
Keywords
Field
DocType
Locality preserving projections (LPP),complex kernel,dimensionality reduction,robust,euler mapping
Kernel (linear algebra),Hilbert space,Discrete mathematics,Trigonometric functions,Dimensionality reduction,Outlier,Euler's formula,Curse of dimensionality,Mathematics,Computational complexity theory
Journal
Volume
ISSN
Citations 
5
2169-3536
2
PageRank 
References 
Authors
0.36
17
5
Name
Order
Citations
PageRank
Qiang Yu1739.88
Rong Wang2637.84
Bing Nan Li324018.77
Xiao-Jun Yang414230.47
Minli Yao522217.30