Kernel And Soft Computing Method Based Pattern Analysis

The digital technologies and computer advances have led to high-dimensional and massive data collection. The following puzzle is how to analysis and utilize so much data. That is a common challenge for pattern analysis, data mining, soft computing and machine learning. In traditional pattern analysis, the computational complexity of many classifiers increases quickly as the number of training samples increases. So when applied to a large data set, those classifiers often become computational intractable. Kernel-based analysis is a powerful new theory of patterns analysis, first appears in the form of support vector machines, and overcomes the computational and statistical difficulties alluded to the traditional learning algorithms. Furthermore, the approach provides a unified framework to reason about and operate on data of all types, such as vectorial, strings, or more complex objects, which is enabling the analysis of a wide variety of patterns. Because of the need of information processing in abundant engineering applications, soft computing methods are used widely. The theory of soft computing developed rapidly, and some other relative theories appear constantly, which increases its importance in the application of data analysis and information processing. This doctoral dissertation investigates the use of kernel methods and soft computing methods in pattern analysis, reveals the practical problems in kernel methods and soft computing methods. These techniques are used to construct novel and effective pattern analysis methods. The dissertation consists of four parts with six chapters:Part 1 is devoted to investigating features analysis algorithm in the kernel feature space. The use of kernel function provides a powerful and principled way of analyzing nonlinear relations using well-understood linear algorithms in an appropriate feature apace. This paper investigates kernel method, principal component analysis and linear discriminant analysis algorithms for proposed the KPL features analysis algorithm. The proposed KPL features analysis algorithm can keep good characteristic of nonlinear relationship of data and the optimal direction of classification.Part 2 contributes to investigate manifold learning algorithm in the kernel feature space. Firstly, a nonlinear dimensionality reduction kernel method based locally linear embedding algorithm is proposed. The proposed algorithm can select the optimal number of nearest neighbors, construct uniform distribution manifold, and overcome the instability of pattern that is caused by the locally linear embedding impressionable the number of nearest neighbors and uniform distribution manifold. ISOMAP is one of widely-used low-dimensional embedding methods. In this paper, we pay our attention to a critical issue that the ISOMAP utilizes local neighborhood information matrices to construct a global embedding of the manifold, is described as Gram matrices, the relation between ISOMAP to Mercer kernel is displayed.Part 3 is to study preconditioning fuzzy support vector machine methods. Firstly, a novel directly constructing fuzzy support vector machine method is presented in order to decreasing originally directly constructing methods sensitivity to noise data, and overcoming disadvantages of the noise data for classification results. The proposed methods integrate fuzzy thoughts, introduce fuzzy compensation, and reconstruct and deduce corresponding optimal problems. Experimental results indicate the proposed methods have higher precision than originally directly constructing methods. Secondly, this paper presents a novel support vector machine based on fuzzy kernel by applying fuzzy theories into SVM's kernel function. The proposed method replaces traditional inner product with the inner product of fuzzy membership value similarity measurement of two samples, where membership value of a sample sufficiently depicts a case of a sample pertaining to a class, and membership value similarity measurement describes two samples' tightness degree pertaining to a class.Part 4 is devoted to investigating the fusion algorithms of kernel methods and soft computing methods. Kernel methods and soft computing methods are used to construct optimized algorithms. Firstly, support vector machine is used for optimizing the fuzzy inference system, SVM reduces the redundant rules and retain the key rules. Secondly, as a new technology, the choice of kernel parameters plays an important role on the kernel-based pattern analysis. Several soft computing algorithms are used to optimize parameter selection problem. Finally, genetic algorithm and rough set theory are used to optimize the adaptive neuro-fuzzy inference system's structure.