Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition
نویسندگان
چکیده
منابع مشابه
Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition
Many previous studies have shown that class classification can be greatly improved by kernel Fisher discriminant analysis (KDA) technique. However, KDA only captures global geometrical structure and disregards local geometrical structure of the data. In this paper, we propose a new feature extraction algorithm, called locality preserving KDA (LPKDA) algorithm. LPKDA first casts KDA as a least s...
متن کاملOptimizing Kernel Function with Applications to Kernel Principal Analysis and Locality Preserving Projection for Feature Extraction
Kernel learning is a popular research topic in pattern recognition and machine learning. Kernel selection is a crucial problem endured by kernel learning method in the practical applications. Many methods of finding the optimal parameters have been presented, but this kind of methods have no ability of changing the kernel structure, accordingly without changing the data distribution in kernel m...
متن کاملGabor Based Optimized Discriminant Locality Preserving Projection for Feature Extraction and Recognition
This paper proposed a Gabor based optimized discriminant locality preserving projections (ODLPP) algorithm which can directly optimize discriminant locality preserving criterion on high-dimensional Gabor feature space via simultaneous diagonalization, without any dimensionality reduction preprocessing. Experimental results conducted on the VALID face database indicate the effectiveness of the p...
متن کاملFisher Locality Preserving Projections for Face Recognition ?
In this paper, a novel dimensionality reduction method termed Fisher Locality Preserving Projections (FLPP) is proposed by introducing the maximum scatter difference criterion (MSDC) to the objective function of Locality Preserving Projections (LPP). FLPP not only inherits the advantages of LPP which attempts to preserve the local structure, but also makes full use of class information and orth...
متن کاملFeature space locality constraint for kernel based nonlinear discriminant analysis
Subspace learning is an important approach in pattern recognition. Nonlinear discriminant analysis (NDA), due to its capability of describing nonlinear manifold structure of samples, is considered to be more powerful to undertake classification tasks in image related problems. In kernel based NDA representation, there are three spaces involved, i.e., original data space, implicitly mapped high ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2013
ISSN: 1875-6891,1875-6883
DOI: 10.1080/18756891.2013.816051