Results 51 to 60 of about 29,752 (189)
IEEE Access, 2018 The extraction of features from the fully connected layer of a convolutional neural network (CNN) model is widely used for image representation. However, the features obtained by the convolutional layers are seldom investigated due to their high ...
Na Liu +5 more
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Eigen‐analysis of nonlinear PCA with polynomial kernels [PDF]
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2013 AbstractThere has been growing interest in kernel methods for classification, clustering and dimension reduction. For example, kernel Fisher discriminant analysis, spectral clustering and kernel principal component analysis are widely used in statistical learning and data mining applications.
Zhiyu Liang, Yoonkyung Lee
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Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics
International Journal of Chemical Engineering, 2022 Traditional onefold data-driven methods for fault detection in complex process industrial systems with high-dimensional, linear, nonlinear, Gaussian, and non-Gaussian coexistence often have less than satisfactory monitoring performance because only a ...
Chenxing Xu +4 more
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Classification and Identification of Industrial Gases Based on Electronic Nose Technology
Sensors, 2019 Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial ...
Hui Li +3 more
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A Locality Preserving Approach for Kernel PCA [PDF]
, 2015 Dimensionality reduction is widely used in image understanding and machine learning tasks. Among these dimensionality reduction methods such as LLE, Isomap, etc., PCA is a powerful and efficient approach to obtain the linear low dimensional space embedded in the original high dimensional space.
Yin Zheng +5 more
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Spectral Geometry for Structural Pattern Recognition [PDF]
, 2010 Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis
El Ghawalby, Heyayda +1 more
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Upper and Lower Bounds on the Performance of Kernel PCA [PDF]
, 2020 27 pagesPrincipal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades. Recently, kernel PCA has emerged as an extension of PCA but, despite its use in practice, a sound theoretical ...
Rivasplata, O +7 more
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Revista Colombiana de Estadística, 2008 In this work, the techniques of Kernel Principal Component Analysis (Kernel PCA or KPCA) and Spectral Clustering are introduced along with some illustrative examples.
Sánchez Luis Gonzalo +2 more
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Principal Polynomial Analysis for Fault Detection and Diagnosis of Industrial Processes
IEEE Access, 2018 Real-time process monitoring is crucial to improve the productivity, process safety, and product quality. In this paper, a novel fault detection and diagnosis technique based on a principal polynomial analysis (PPA) is proposed.
Xinmin Zhang, Manabu Kano, Yuan Li
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Revista Colombiana de Estadística, 2008 En el presente trabajo, se introducen las técnicas de kernel ACP (KACP) y conglomeramiento espectral con algunos ejemplos ilustrativos. Se pretende estudiar los efectos de aplicar ACP como preproceso sobre las observaciones que se desean agrupar, para lo
LUIS GONZALO SÁNCHEZ +2 more
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