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In this paper, a supervised fuzzy clustering RBF neural network (SFC-RBFNN) based on kernel PCA is introduced for constructing the fabric sewability evaluation system. Our experimental results demonstrate that the proposed system could efficiently be used as an objective seam pucker evaluation system with high accuracy and is robust for various structures and mechanical properties of middle-thickness...
Principal components analysis (PCA) is an important approach to unsupervised dimensionality reduction. However, principal components (PCs) are a set of new variables carrying no clear physical meanings and still require all the original variables. To deal with this problem, the PC dominant feature (PCDF) is defined. Then, feature selection using them is considered and a new algorithm for determining...
Facial attribute-specific subspace-based PCA (FASS-based PCA) considers the information of class labels, and the discriminant power can be improved. However, it doesn't consider the outliers which are .common in realistic training sets. To address this problem, we propose robust facial attribute-specific subspace-based PCA (robust FASS-based PCA) algorithm in this paper, which gives a new weighted...
Least squares support vector machine (LSSVM) has been used in soft sensor modeling in recent years. In developing a successful model based on LSSVM, the first important step is feature extraction. Principal components analysis (PCA) is a usual method for linear feature extraction and kernel PCA (KPCA) is a nonlinear PCA developed by using the kernel method. KPCA can efficiently extract the nonlinear...
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