Results 31 to 40 of about 275,774 (266)

Accelerated random noise suppression of seismic data using compressed singular-value decomposition

矿业科学学报
Random noise is one of the common background noises in seismic data, and its attenuation will directly affect the signal-to-noise ratio of seismic data, which is of great significance to improve the quality of seismic data.
SUN Chao, LIN Peng, LIU Yulin, WANG Xiudong, XU Dongjing   +4 more
doaj   +1 more source

Statistical inference based on robust low-rank data matrix approximation

, 2014
The singular value decomposition is widely used to approximate data matrices with lower rank matrices. Feng and He [Ann. Appl. Stat. 3 (2009) 1634-1654] developed tests on dimensionality of the mean structure of a data matrix based on the singular value ...
Feng, Xingdong, He, Xuming
core   +1 more source

Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces

, 2020
Reproducing kernel Hilbert spaces (RKHSs) play an important role in many statistics and machine learning applications ranging from support vector machines to Gaussian processes and kernel embeddings of distributions.
A Berlinet, A Lasota, B Schölkopf, C Baker, G Froyland, GH Golub, H Engl, I Mezić, I Steinwart, J Shawe-Taylor, J Weidmann, K Fukumizu, K Muandet, M Reed, N Aronszajn, P Koltai, R Eubank, S Klus, S Klus, S Klus, T Kato, T Melzer   +21 more
core   +1 more source

A Modified Singular Value Decomposition (MSVD) Approach for the Enhancement of CCTV Low-Quality Images

IEEE Access
Image enhancement and reconstruction is an important field of research in digital image analysis. To increase the quality of low-contrast images, a variety of image-enhancing technologies are available.
Shahzada Fahad, Sami Ur Rahman, Fakhre Alam, Muhammad Yousaf, Faten S. Alamri, Naveed Abbas, Saeed Ali Bahaj, Amjad R. Khan   +7 more
doaj   +1 more source

Split-and-Combine Singular Value Decomposition for Large-Scale Matrix

Journal of Applied Mathematics, 2013
The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, high- dimensional data visualization, dimension reduction, data mining, latent semantic analysis,
Jengnan Tzeng
doaj   +1 more source

The Singular Value Decomposition over Completed Idempotent Semifields

Mathematics, 2020
In this paper, we provide a basic technique for Lattice Computing: an analogue of the Singular Value Decomposition for rectangular matrices over complete idempotent semifields (i-SVD).
Francisco J. Valverde-Albacete, Carmen Peláez-Moreno   +1 more
doaj   +1 more source

Identification of Seismic Reflections Using Singular Value Decomposition [PDF]

Modeling, Identification and Control, 1986
Singular value decomposition (SVD) is applied to the identification of seismic reflections by using two different models: the impulse response model where a seismic trace is assumed to consist of a known signal pulse convolved with a reflection ...
Bjørn Ursin, Yuying Zheng
doaj   +1 more source

Very Large-Scale Singular Value Decomposition Using Tensor Train Networks [PDF]

, 2014
We propose new algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format. The proposed algorithms can compute several dominant singular values and
Cichocki, Andrzej, Lee, Namgil
core   +1 more source

Peroxidasin enables melanoma immune escape by inhibiting natural killer cell cytotoxicity

Molecular Oncology, EarlyView.
Peroxidasin (PXDN) is secreted by melanoma cells and binds the NK cell receptor NKG2D, thereby suppressing NK cell activation and cytotoxicity. PXDN depletion restores NKG2D signaling and enables effective NK cell–mediated melanoma killing. These findings identify PXDN as a previously unrecognized immune evasion factor and a potential target to improve
Hsu‐Min Sung, David Bickel, Lena C. M. Krause, Daria Ezeriņa, Christian Ickes, Julian Wojtachnia, Christine S. Gibhardt, Magdalena Shumanska, Khadija Wahni, Andrea Paluschkiwitz, Julia Malo Pueyo, Ekaterina Baranova, Wim Vranken, Hedwig Stanisz, Ioana Stejerean‐Todoran, Michael P. Schön, Joris Messens, Ivan Bogeski   +17 more
wiley   +1 more source

Singular Value Decomposition Wavelength-Multiplexing Ghost Imaging

Photonics
To enhance imaging quality, singular value decomposition (SVD) has been applied to single-wavelength ghost imaging (GI) or color GI. In this paper, we extend the application of SVD to wavelength-multiplexing ghost imaging (WMGI) for reducing the ...
Yingtao Zhang, Xueqian Zhang, Zongguo Li, Hongguo Li   +3 more
doaj   +1 more source