Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces
نویسندگان
چکیده
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher’s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.
منابع مشابه
An extended feature set for blind image steganalysis in contourlet domain
The aim of image steganalysis is to detect the presence of hidden messages in stego images. We propose a blind image steganalysis method in Contourlet domain and then show that the embedding process changes statistics of Contourlet coefficients. The suspicious image is transformed into Contourlet space, and then the statistics of Contourlet subbands coefficients are extracted as features. We us...
متن کاملAutomatic Feature Selection via Weighted Kernels and Regularization
Selecting important features in non-linear kernel spaces is a difficult challenge in both classification and regression problems. We propose to achieve feature selection by optimizing a simple criterion: a feature-regularized loss function. Features within the kernel are weighted, and a lasso penalty is placed on these weights to encourage sparsity. We minimize this feature-regularized loss fun...
متن کاملA Geometry Preserving Kernel over Riemannian Manifolds
Abstract- Kernel trick and projection to tangent spaces are two choices for linearizing the data points lying on Riemannian manifolds. These approaches are used to provide the prerequisites for applying standard machine learning methods on Riemannian manifolds. Classical kernels implicitly project data to high dimensional feature space without considering the intrinsic geometry of data points. ...
متن کاملUnsupervised discriminative feature selection in a kernel space via L2, 1-norm minimization
Traditional nonlinear feature selection methods map the data from an original space into a kernel space to make the data be separated more easily, then move back to the original feature space to select features. However, the performance of clustering or classification is better in the kernel space, so we are able to select the features directly in the kernel space and get the direct importance ...
متن کاملRecognition of Expressions Based on Kernel Global and Local Symmetrical Weighted Fisher Discriminant Nonlinear Subspace Approach
Preservation of global and local features of images during dimensional reduction is a challenging task. The main goal of this work is to resolve the problem of singularity matrix by preserving local and global discriminative features by introducing symmetrical weights on principal components. To meet this goal Combinational Entire Gabor Kernel Global and Locality Preserving Symmetrical Weighted...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- IEEE Trans. Pattern Anal. Mach. Intell.
دوره 25 شماره
صفحات -
تاریخ انتشار 2003