Results 131 to 140 of about 192,804 (181)

A two-tier strategy for developing water deficit stress tolerant maize: hydroponics-based root phenotyping followed by rainfed field validation. [PDF]

Front Plant Sci
Pati R, Sandhu S, Vikal Y, Gowda M, Zaidi PH, Ranjan R, Vinayan MT, Vivek BS, Sharda R, Garg T.   +9 more
europepmc   +1 more source

Principal component guided DCGAN for mitigating mode collapse in electromagnetic pulse signal synthesis. [PDF]

Sci Rep
Gao Z, Zhao L, Zheng J, Yi T, Zhang C, Wang Q.   +5 more
europepmc   +1 more source

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Robust kernel principal component analysis with optimal mean

Neural Networks, 2022
The kernel principal component analysis (KPCA) serves as an efficient approach for dimensionality reduction. However, the KPCA method is sensitive to the outliers since the large square errors tend to dominate the loss of KPCA. To strengthen the robustness of KPCA method, we propose a novel robust kernel principal component analysis with optimal mean ...
Pei Li, Wenlin Zhang, Chengjun Lu, Rui Zhang, Xuelong Li   +4 more
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Robust Kernel Principal Component Analysis

Neural Computation, 2009
This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions
Huang S.-Y., Yeh Y.-R., Eguchi S.
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Incremental Kernel Principal Component Analysis

IEEE Transactions on Image Processing, 2007
The kernel principal component analysis (KPCA) has been applied in numerous image-related machine learning applications and it has exhibited superior performance over previous approaches, such as PCA. However, the standard implementation of KPCA scales badly with the problem size, making computations for large problems infeasible.
Chin, T., Suter, D.
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Kernel Principal Component Analysis

1997
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images.
Schölkopf, B., Smola, A., Müller, K.
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