Results 31 to 40 of about 29,752 (189)
Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization
Cybernetics and Information Technologies, 2014 In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix ...
Bai, Lin, Li Yanbo, Hui Meng
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Missing Data in Kernel PCA [PDF]
, 2006 Kernel Principal Component Analysis (KPCA) is a widely used technique for visualisation and feature extraction. Despite its success and flexibility, the lack of a probabilistic interpretation means that some problems, such as handling missing or corrupted data, are very hard to deal with.
Guido Sanguinetti, Neil D. Lawrence
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Energies, 2022 Wind turbine operators usually use data from a Supervisory Control and Data Acquisition system to monitor their conditions, but it is challenging to make decisions about maintenance based on hundreds of different parameters.
Panagiotis Korkos +3 more
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Interactive Knowledge-Based Kernel PCA [PDF]
, 2014 Data understanding is an iterative process in which domain experts combine their knowledge with the data at hand to explore and confirm hypotheses. One important set of tools for exploring hypotheses about data are visualizations. Often, however, traditional, unsupervised dimensionality reduction algorithms are used for visualization. These tools allow
Dino Oglic +2 more
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Remote Sensing, 2020 In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are embedded in a multiple kernel learning framework to simultaneously exploit the local and multiscale information in both spatial and spectral dimensions for hyperspectral
Lei Pan, Chengxun He, Yang Xiang, Le Sun
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IEEE Access, 2021 Single traditional multivariate statistical monitoring methods, such as principal component analysis (PCA) and canonical variate analysis (CVA), are less effective in nonlinear dynamic processes.
Liangliang Shang +4 more
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IEEE Access, 2020 Kernel Principal Component Analysis (KPCA) using Radial Basis Function (RBF) kernels can capture data nonlinearity by projecting the original variable space to a high-dimensional kernel feature space and obtaining the kernel principal components.
Ruomu Tan +2 more
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Incremental kernel PCA and the Nyström method
CoRR, 2018 Incremental versions of batch algorithms are often desired, for increased time efficiency in the streaming data setting, or increased memory efficiency in general. In this paper we present a novel algorithm for incremental kernel PCA, based on rank one updates to the eigendecomposition of the kernel matrix, which is more computationally efficient than ...
Fredrik Hallgren, Paul Northrop
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, 2014 As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Han, Junwei +6 more
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Sparse Kernel PCA for Outlier Detection
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018 Accepted at IEEE ICMLA 2018 for Oral ...
Rudrajit Das +2 more
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