Approximation-free running SVD and its application to motion detection

Chetverikov, Dmitry; Axt, A.

doi:10.1016/j.patrec.2009.12.031

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…Furthermore, we calculated the KLT basis by using the SVD decomposition of 60 successive beats. The running SVD algorithm [15] were used to improve the speed of the preprocessing step. For further analysis see Chapters 5 and Appendix 9A from book [12].…”

Section: Methodsmentioning

confidence: 99%

RAIT: the Rational Approximation and Interpolation Toolbox for Matlab, with Experiments on ECG Signals

Kovács

Lócsi

2012

International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems

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Abstract-There is a wide range of applications of rational function systems. Including in system, control theories and signal processing. A special class of rational functions, the so-called Blaschke functions and the orthonormal Malmquist-Takenaka (MT) systems are effectively used for representing signals especially electrocardiograms.We present our project on a general MATLAB library for rational function systems and their applications. It contains Blaschke functions, MT systems and biorthogonal systems. We implemented not only the continuous but the discrete versions as well, since in applications the latter one is needed. The complex and real interpretations are both available. We also built in methods for finding the poles automatically. Also, some interactive GUIs were implemented for visual demonstration that help the users in understanding the roles of certain parameters such as poles, multiplicity etc.

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Section: Methodsmentioning

confidence: 99%

RAIT: the Rational Approximation and Interpolation Toolbox for Matlab, with Experiments on ECG Signals

Kovács

Lócsi

2012

International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems

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“…The literature [24] presents a fast approximation-free incremental SVD which is an updating procedure adding the entering data to the original matrix and to calculate the SVD of the extended matrix based on the previous SVD. The update procedure of incremental SVD algorithm is as follows:…”

Section: Incremental Svd Algorithmmentioning

confidence: 99%

Scalable approximating SVD algorithm for recommender systems

Zhou

Huang

et al. 2014

Web Intelligence and Agent Systems: An International Journal

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With the rapid development of Internet, the amount of information on the Web grows explosively, people often feel puzzled and helpless in finding and getting the information they really need. For overcoming this problem, recommender systems such as singular value decomposition (SVD) method help users finding relevant information, products or services by providing personalized recommendations based on their profiles. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data.Thus, to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm which is called ApproSVD algorithm based on approximating SVD in this paper. The trick behind our algorithm is to sample some rows of a user-item matrix, rescale each row by an appropriate factor to form a relatively smaller matrix, and then reduce the dimensionality of the smaller matrix. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on MovieLens dataset and Flixster dataset, and show that our method has the best prediction quality. Furthermore, in order to show the superiority of the ApproSVD algorithm, we also conduct an empirical study to compare the prediction accuracy and running time between ApproSVD algorithm and incremental SVD algorithm on MovieLens dataset and Flixster dataset, and demonstrate that our proposed method has better performance overall.

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“…However, as suggested by Gu and Eisenstat (1994) and extended by Chetverikov and Axt (2010) and Vogel et al (2012), the SVD can be computed using an incremental scheme, matching the incremental nature of collecting data while moving the detector.…”

Section: Singular Valuesmentioning

confidence: 99%