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Tytuł:
Analysis of dynamics of gene exspression using singular value decomposition
Autorzy:
Simek, K.
Kimmel, M.
Powiązania:
https://bibliotekanauki.pl/articles/332865.pdf
Data publikacji:
2002
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
wielokrotna ekspresja genów
dynamiczny model danych ekspresji genów
pojedynczy rozkład wartości
multiple gene expression
dynamic model of gene expression data
singular value decomposition
Opis:
Recently, data on multiple gene expression at sequential time points were analyzed, using Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by fitting of a linear discrete-time dynamical model in which the expression values at a given time point are linear combinations of the values at a previous time point. We attempt to address several aspects of the method. To obtain the model we formulate a nonlinear optimization problem and present how to solve it numerically using standard MATLAB procedures. We use publicly available data to test the approach. Then, we investigate the sensitivity of the method to missing measurements and its possibilities to reconstruct missing data. Summarizing we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics but may also lead to unexpected difficulties.
Źródło:
Journal of Medical Informatics & Technologies; 2002, 3; MI31-40
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid watermarking scheme based on singular value decomposition ghost imaging
Autorzy:
Wu, Jun-Yun
Huang, Wei-Liang
Wen, Ru-Hong
Gong, Li-Hua
Powiązania:
https://bibliotekanauki.pl/articles/1835818.pdf
Data publikacji:
2020
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
digital watermarking
SVD ghost imaging
discrete wavelet transform
imperceptibility
Opis:
A hybrid watermarking algorithm with an optical watermark image based on singular value decomposition (SVD) ghost imaging is designed. Simultaneously, the blended watermarking algorithm is designed based on 4-level discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The 4-level diagonal sub-band image is obtained by performing 4-level two-dimensional wavelet transform on the original image, and the coefficient matrix is produced by applying the discrete cosine transform on the 4-level diagonal sub-band image. Then, three matrices are obtained by performing the singular value decomposition on the coefficient matrix. In addition, the optical watermark image is encrypted by an SVD ghost imaging system. The system could generate a secret key, and unauthorized users could not decrypt and reconstruct the original watermark image without this key. Later the encrypted watermark image is generated into the other three matrices by singular value decomposition. Afterwards, the encrypted watermark is embedded in the host image by mutual operation of different matrices in the algorithm. Simulation results validate the feasibility of the proposed hybrid watermarking scheme.
Źródło:
Optica Applicata; 2020, 50, 4; 633--647
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A fast neural network learning algorithm with approximate singular value decomposition
Autorzy:
Jankowski, Norbert
Linowiecki, Rafał
Powiązania:
https://bibliotekanauki.pl/articles/330870.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
Moore–Penrose pseudoinverse
radial basis function network
extreme learning machine
kernel method
machine learning
singular value decomposition
deep extreme learning
principal component analysis
pseudoodwrotność Moore–Penrose
radialna funkcja bazowa
maszyna uczenia ekstremalnego
uczenie maszynowe
analiza składników głównych
Opis:
The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks. The main idea of this algorithm lies in the usage of fast approximation of the Moore–Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O(mn2). We consider algorithms with a complexity of O(mnl), where l < n and l is often significantly smaller than n. Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 581-594
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using Singular Value Decomposition (SVD) as a solution for search result clustering
Autorzy:
Abdulla, H. D.
Abdelrahman, A. S.
Snasel, V.
Powiązania:
https://bibliotekanauki.pl/articles/377259.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
singular value decomposition
clustering
self-organizing map
Opis:
There are many search engines in the web, but they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Singular Value Decomposition (SVD) as a very good solution for search results clustering. Results are presented by visualizing neural network. Neural network is responsive for reducing result dimension to two dimensional space and we are able to present result as a picture that we are able to analyze.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2014, 80; 71-78
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Properties of a Singular Value Decomposition Based Dynamical Model of Gene Expression Data
Autorzy:
Simek, K.
Powiązania:
https://bibliotekanauki.pl/articles/908156.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
informatyka
multiple gene expression
singular value decomposition
dynamical model of gene expression data
Opis:
Recently, data on multiple gene expression at sequential time points were analyzed using the Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by the fitting of a linear discrete-time dynamical system in which the expression values at a given time point are linear combinations of the values at a previous time point. We attempt to address several aspects of the method. To obtain the model, we formulate a nonlinear optimization problem and present how to solve it numerically using the standard MATLAB procedures. We use freely available data to test the approach. We discuss the possible consequences of data regularization, called sometimes "polishing", on the outcome of the analysis, especially when the model is to be used for prediction purposes. Then, we investigate the sensitivity of the method to missing measurements and its abilities to reconstruct the missing data. Summarizing, we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics, but may also lead to unexpected difficulties, like overfitting problems.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 3; 337-345
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Color image encryption using singular value decomposition in discrete cosine Stockwell transform domain
Autorzy:
Vaish, A.
Kumar, M.
Powiązania:
https://bibliotekanauki.pl/articles/173700.pdf
Data publikacji:
2018
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
discrete cosine Stockwell transform
image encryption
singular value decomposition
mean square error
structural similarity index measurement
Opis:
In this paper, an image encryption technique using singular value decomposition (SVD) and discrete cosine Stockwell transform (DCST) is proposed. The original source image is encrypted using bands of DCST along with the SVD decomposed images. The number of bands in DCST, parameters used to mask the singular values, the way of permutation used to shuffle the values of SVD transformed images and the way of arrangement of SVD matrices are used as encryption keys. It is necessary to have correct knowledge of all the keys along with their respective values, for correct decryption of encrypted images. The robustness and the quality measurement of proposed work are analyzed by comparing it with some existing works.
Źródło:
Optica Applicata; 2018, 48, 1; 25-38
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of the possibility of using the singular value decomposition in image compression
Autorzy:
Łukasik, Edyta
Łabuć, Emilia
Powiązania:
https://bibliotekanauki.pl/articles/38432811.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
singular value decomposition
JPEG 2000
JPEG
discrete wavelet transform
discrete cosine transform
Opis:
In today’s highly computerized world, data compression is a key issue to minimize the costs associated with data storage and transfer. In 2019, more than 70% of the data sent over the network were images. This paper analyses the feasibility of using the SVD algorithm in image compression and shows that it improves the efficiency of JPEG and JPEG2000 compression. Image matrices were decomposed using the SVD algorithm before compression. It has also been shown that as the image dimensions increase, the fraction of eigenvalues that must be used to reconstruct the image in good quality decreases. The study was carried out on a large and diverse set of images, more than 2500 images were examined. The results were analyzed based on criteria typical for the evaluation of numerical algorithms operating on matrices and image compression: compression ratio, size of compressed file, MSE, number of bad pixels, complexity, numerical stability, easiness of implementation.
Źródło:
Applied Computer Science; 2022, 18, 4; 53-67
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Spatial-Temporal Analysis of Rainfall West Java Indonesia Using Empirical Orthogonal Function based on Singular Value Decomposition
Autorzy:
Pribadi, Diantiny Mariam
Sumiati, Ira
Purwani, Sri
Powiązania:
https://bibliotekanauki.pl/articles/1031922.pdf
Data publikacji:
2020
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Empirical Orthogonal Function
Rainfall
Singular Value Decomposition
Spatial-Temporal
Opis:
Rainfall is one of the climate variables that have a significant influence, especially in supporting the activities of various sectors in tropical countries. Climate change is causing rainfall variability in Indonesia. However, the analysis of climate variable patterns is difficult because of the formation of a large matrix. Empirical Orthogonal Function (EOF) analysis can be used to reduce the dimensions of large data by maintaining as much variation as possible from the original data set. The method used in this study is through the Singular Value Decomposition (SVD) approach. The analysis shows that 98.50% of the total rainfall variance can be represented by four EOF modes. Analysis of the spatial pattern of EOF1 shows that rainfall is below average, while the other EOF modes show variations in rainfall.
Źródło:
World Scientific News; 2020, 140; 113-126
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Generalized singular value decomposition in multidimensional condition monitoring of systems
Uogólniony rozkład wartości szczególnych w wielowymiarowej diagnostyce stanu systemów
Autorzy:
Cempel, C.
Powiązania:
https://bibliotekanauki.pl/articles/329250.pdf
Data publikacji:
2008
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
stan techniczny
wielowymiarowość
uogólnione SVD
przestrzeń uszkodzeń
przestrzeń obserwacji
podobieństwo stanu
machine condition
multidimensional
generalized SVD
observation space
fault space
condition similarity
Opis:
With the modern metrology, we can measure almost all variables in the phenomenon field of a working machine, and much of measuring quantities can be symptoms of machine condition. On this basis, we can form the symptom observation matrix (SOM) for condition monitoring. From the other side we know that contemporary complex machines may have many modes of failure, so called faults, which form the fault space. This multidimensional problem is not a simple one, even if we apply some modern tool like SVD for the fault extraction purpose. So the question remains if one can learn considering similar problem when having SOM of similar machine observed just before. In this way, we can consider the application of generalized GSVD to the machine condition monitoring problems, and uncover some new possibilities.
Obecnie potrafimy mierzyć większość procesów pola zjawiskowego pracującej maszyny, a wiele z tych procesów może dostarczyć symptomów jej stanu technicznego. Wychodząc stąd możemy tworzyć symptomową macierz obserwacji (SOM) do celów diagnostyki maszyn, czyli oceny ewolucji jej stanu technicznego w czasie życia [theta]. Ale współczesne maszyny mają wiele uszkodzeń rozwijających się współbieżnie, stąd tez propozycja diagnostyki wielowymiarowej i zastosowania rozkładu (SVD), co pokazano już w wielu pracach. Powstaje pytanie czy potrafimy uzyskana wiedzę wykorzystać i nauczyć się diagnozować lepiej maszyny, które już są rozpoznane diagnostycznie za pomocą SVD. Taki właśni problem postawiono stosując uogólniony rozkład SVD, umożliwiający porównanie dwu macierzy obserwacji, znanej uprzednio i właśnie rozwijającej się. Tak możliwość istnieje, a stawia przed nami nowe wymogi nauczenia się nowej semantyki wspólnego języka GSVD.
Źródło:
Diagnostyka; 2008, 3(47); 23-30
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A study of parallel techniques for dimensionality reduction and its impact on the quality of text processing algorithms
Autorzy:
Pietroń, M.
Wielgosz, M.
Karwatowski, M.
Wiatr, K.
Powiązania:
https://bibliotekanauki.pl/articles/114190.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
singular value decomposition
vector space model
TFIDF
Opis:
The presented algorithms employ the Vector Space Model (VSM) and its enhancements such as TFIDF (Term Frequency Inverse Document Frequency) with Singular Value Decomposition (SVD). TFIDF were applied to emphasize the important features of documents and SVD was used to reduce the analysis space. Consequently, a series of experiments were conducted. They revealed important properties of the algorithms and their accuracy. The accuracy of the algorithms was estimated in terms of their ability to match the human classification of the subject. For unsupervised algorithms the entropy was used as a quality evaluation measure. The combination of VSM, TFIDF, and SVD came out to be the best performing unsupervised algorithm with entropy of 0.16.
Źródło:
Measurement Automation Monitoring; 2015, 61, 7; 352-353
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-10 z 56

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