Results 1 to 10 of about 128,325 (262)
Robust Differentiable SVD [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms. However, the derivatives of the eigenvectors tend to be numerically unstable, whether using the SVD to compute them analytically or using the Power Iteration (PI) method to approximate them.
Wei Wang +4 more
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Dimensionality Reduction: Challenges and Solutions [PDF]
ITM Web of Conferences, 2022 The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dimensional data. These techniques gather several data features of interest, such as dynamical structure, input-output relationships, the correlation between
Ahmad Noor, Nassif Ali Bou
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Journal of High Energy Physics, 2023 Abstract In this paper, we introduce a new quantity called SVD entanglement entropy. This is a generalization of entanglement entropy in that it depends on two different states, as in pre- and post-selection processes. This SVD entanglement entropy takes non-negative real values and is bounded by the logarithm of the Hilbert space ...
Arthur J. Parzygnat +3 more
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Journal Focus Action of Research Mathematic, 2022 Model Generalized AMMI (GAMMI) merupakan perluasan dari model AMMI (Additive Main Effect and Multiplicative Interaction). Model GAMMI melibatkan konsep Generalized Linear Model (GLM) pada variabel responnya.
Kurnia Ahadiyah, Ardiana Fatma Dewi
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Introducing a New Hybrid Adaptive Local Optimal Low Rank Approximation Method for Denoising Images [PDF]
International Journal of Industrial Electronics, Control and Optimization, 2020 This paper aimed to formulate image noise reduction as an optimization problem and denoise the target image using matrix low rank approximation. Considering the fact that the smaller pieces of an image are more similar (more dependent) in natural images;
sadegh kalantari +2 more
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IEEE Transactions on Image Processing, 2021 This work considers noise removal from images, focusing on the well known K-SVD denoising algorithm. This sparsity-based method was proposed in 2006, and for a short while it was considered as state-of-the-art. However, over the years it has been surpassed by other methods, including the recent deep-learning-based newcomers.
Meyer Scetbon +2 more
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A COURTYARD HOUSE – SIHEYUAN 四合院 AS THE DWELLING PLACE OF THE TRADITIONAL CHINESE FAMILY
Forum Teologiczne, 2020 A Chinese courtyard house, called in Chinese siheyuan, equipped with a single entrance and with one or more open courtyards encompassed by one-storey buildings, represents traditional house dwelling in China.
Zbigniew Wesołowski SVD
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Content-based image retrieval using a fusion of global and local features
ETRI Journal, 2023 Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance.
Hee Hyung Bu, Nam Chul Kim, Sung Ho Kim
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Izravnava po metodi najmanjših kvadratov z upoštevanjem pogreškov pri neznankah (= Least-squares adjustment taking into account the errors in variables) [PDF]
Geodetski Vestnik, 2021 In this article, we discuss the procedure for computing the values of the unknowns under the condition of the minimum sum of squares of the observation residuals (least-squares method), taking into account the errors in the unknowns.
Aleš Marjetič
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Rolling Bearings Fault Diagnosis Method Integrating CEEMDAN-SVD and Cepstrum
Taiyuan Ligong Daxue xuebao, 2021 Aiming at the non-stationary time-varying characteristics of rolling bearing fault signals, a fault diagnosis method for rolling bearings was proposed, which integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN ...
Jinni ZHENG, Jie BIAN
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