Optimizing Kernel Function with Applications to Kernel Principal Analysis and Locality Preserving Projection for Feature Extraction
نویسندگان
چکیده
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 mapping space. In this paper, we present a uniform framework of kernel optimization based on data-dependent kernel from theory to applications to kernel principal analysis and locality preserving projection for feature extraction. Some experiments are implemented to evaluate the performance and feasibility of this framework. Feature extraction, machine learning, kernel principal analysis, locality preserving projection
منابع مشابه
A 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 کاملSupervised Composite Kernel Locality Preserving Projection Feature Extraction for Hyperspectral Image Classification
Locally preserving projection (LPP) does not take advantage of the spatial correlation of pixels in the image, and the pixels are considered as independent pieces of information. In this paper, a kernel based manifold learning feature extraction method which considers spatial relationship of neighboring pixels, called supervised composite kernel locality preserving projection (SCKLPP), is propo...
متن کاملSupervised Feature Extraction of Face Images for Improvement of Recognition Accuracy
Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images ar...
متن کاملA common strategy for extracting the feature and to preserve the global structure such as Principal Component Analysis, Two Dimensional Principal Component Analysis and Linear Discriminant Analysis
A common strategy for extracting the feature and to preserve the global structure such as Principal Component Analysis, Two Dimensional Principal Component Analysis and Linear Discriminant Analysis have been used. These schemes are a classical linear technique that projects the data along the directions of maximum variance. To improve the performance, Locality Preserving Projection is used. The...
متن کاملA Least-Squares Framework for Component Analysis (Under review for publication in PAMI)
Over the last century, Component Analysis (CA) methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Locality Preserving Projections (LPP), and Spectral Clustering (SC) have been extensively used as a feature extraction step for modeling, classification, visualization, and clustering. CA techniques are appealing because man...
متن کامل