Journal ArticleDOI
Kernel common discriminant-based multimodal image sensor data classification
TLDR
A novel image recognition method of kernel common discriminant based image classification by extending DCV with kernel trick with the space isomorphic mapping view in the kernel feature space and developing a two-phase algorithm of KPCA + DCV.About:
This article is published in Measurement.The article was published on 2014-02-01. It has received 1 citations till now. The article focuses on the topics: Radial basis function kernel & Kernel (image processing).read more
Citations
More filters
Journal ArticleDOI
Kernel-based discriminative elastic embedding algorithm
TL;DR: A nonlinear version of discriminative elastic embedding (DEE) algorithm is presented, called kernel discriminatives elastic embeddedding (KDEE), and a deliberately selected Laplacian search direction is adopted in KDEE1 for faster convergence.
References
More filters
Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI
A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Proceedings Article
Locality Preserving Projections
Xiaofei He,Partha Niyogi +1 more
TL;DR: These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold.
Related Papers (5)
An Image Classification Method Based on Multi-feature Fusion and Multi-kernel SVM
Zixi Xiang,Xueqiang Lv,Kai Zhang +2 more