The Experts below are selected from a list of 21765 Experts worldwide ranked by ideXlab platform
Yehoshua Y. Zeevi - One of the best experts on this subject based on the ideXlab platform.
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Oversampling in the Gabor scheme
IEEE Transactions on Signal Processing, 1993Co-Authors: Meir Zibulski, Yehoshua Y. ZeeviAbstract:A method for calculating the coefficients of the Gabor expansion in the case of Oversampling is presented. The method is based on the concept of frames and utilizes the Zak transform. As such, the Zak transform highlights the meaning and importance of frames in the context of Oversampling, and other aspects of signal representation by means of nonorthogonal bases. >
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ICASSP - Oversampling in the Gabor scheme
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics Speech and Signal Processing, 1992Co-Authors: Meir Zibulski, Yehoshua Y. ZeeviAbstract:A method for calculating the coefficients of the Gabor expansion in the context of Oversampling is presented. The method is based on the concept of frames and utilizes the Zak transform. The Zak transform further highlights the meaning and importance of frames in the context of Oversampling and other aspects of signal representation by means of nonorthogonal bases. >
Jakob Lemvig - One of the best experts on this subject based on the ideXlab platform.
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Oversampling of wavelet frames for real dilations
Journal of the London Mathematical Society, 2012Co-Authors: Marcin Bownik, Jakob LemvigAbstract:We generalize the Second Oversampling Theorem for wavelet frames and dual wavelet frames from the setting of integer dilations to real dilations. We also study the relationship between dilation matrix Oversampling of semi-orthogonal Parseval wavelet frames and the additional shift invariance gain of the core subspace. Oversampling of wavelet frames has been a subject of extensive study by several authors dating back to the early 1990s. The first Oversampling results are due to Chui and Shi [16, 17], who proved that Oversampling by odd factors preserves tightness of dyadic affine frames. This is now the central result of the subject known as the Second Oversampling Theorem. Its higher dimensional generalizations to integer matrix dilations were studied by Chui and Shi [18], Johnson [27], Laugesen [30], and Ron and Shen [31]. In particular, these authors introduced (in several equivalent forms) the class of Oversampling matrices ‘relatively prime’ to a fixed dilation A and they established several Oversampling results for (not necessarily tight) affine frames. Dutkay and Jorgensen [23] shed a new light on these results by showing that Oversampling of orthonormal (or frame) wavelets by such matrices leads to orthonormal (or frame) superwavelets, respectively. Chui and Sun [22] have completed the understanding of the case of integer dilations by showing that the class of ‘relatively prime’ matrices is optimal for the Second Oversampling Theorem; that is, if an Oversampling matrix falls out of this class, then the Oversampling does not preserve a tight frame property in general. However, it is possible to give a characterization of Oversampling matrices preserving tightness once affine frame generators are chosen. These
Meir Zibulski - One of the best experts on this subject based on the ideXlab platform.
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Oversampling in the Gabor scheme
IEEE Transactions on Signal Processing, 1993Co-Authors: Meir Zibulski, Yehoshua Y. ZeeviAbstract:A method for calculating the coefficients of the Gabor expansion in the case of Oversampling is presented. The method is based on the concept of frames and utilizes the Zak transform. As such, the Zak transform highlights the meaning and importance of frames in the context of Oversampling, and other aspects of signal representation by means of nonorthogonal bases. >
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ICASSP - Oversampling in the Gabor scheme
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics Speech and Signal Processing, 1992Co-Authors: Meir Zibulski, Yehoshua Y. ZeeviAbstract:A method for calculating the coefficients of the Gabor expansion in the context of Oversampling is presented. The method is based on the concept of frames and utilizes the Zak transform. The Zak transform further highlights the meaning and importance of frames in the context of Oversampling and other aspects of signal representation by means of nonorthogonal bases. >
Iman Nekooeimehr - One of the best experts on this subject based on the ideXlab platform.
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adaptive semi unsupervised weighted Oversampling a suwo for imbalanced datasets
Expert Systems With Applications, 2016Co-Authors: Iman Nekooeimehr, Susana K LaiyuenAbstract:A new Oversampling method for imbalanced dataset classification is presented.It clusters the minority class and identifies borderline minority instances.Considering majority class during minority class clustering improves Oversampling.Cluster size after Oversampling should be dependent on its misclassification error.Generated synthetic instances improved subsequent classification. In many applications, the dataset for classification may be highly imbalanced where most of the instances in the training set may belong to one of the classes (majority class), while only a few instances are from the other class (minority class). Conventional classifiers will strongly favor the majority class and ignore the minority instances. In this paper, we present a new Oversampling method called Adaptive Semi-Unsupervised Weighted Oversampling (A-SUWO) for imbalanced binary dataset classification. The proposed method clusters the minority instances using a semi-unsupervised hierarchical clustering approach and adaptively determines the size to oversample each sub-cluster using its classification complexity and cross validation. Then, the minority instances are oversampled depending on their Euclidean distance to the majority class. A-SUWO aims to identify hard-to-learn instances by considering minority instances from each sub-cluster that are closer to the borderline. It also avoids generating synthetic minority instances that overlap with the majority class by considering the majority class in the clustering and Oversampling stages. Results demonstrate that the proposed method achieves significantly better results in most datasets compared with other sampling methods.
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Cluster-based Weighted Oversampling for Ordinal Regression (CWOS-Ord)
Neurocomputing, 2016Co-Authors: Iman Nekooeimehr, Susana K. Lai-yuenAbstract:A new Oversampling method called Cluster-based Weighted Oversampling for Ordinal Regression (CWOS-Ord) is proposed for addressing ordinal regression with imbalanced datasets. Ordinal regression is a supervised approach for learning the ordinal relationship between classes. In many applications, the dataset is highly imbalanced where the instances of some classes (majority classes) occur much more frequently than instances of other classes (minority classes). This significantly degrades the classification performance as classifiers tend to strongly favor the majority classes. Standard Oversampling methods can be used to improve the dataset class distribution; however, they do not consider the ordinal relationship between the classes. The proposed CWOS-Ord method aims to address this problem by first clustering minority classes and then Oversampling them based on their distances and ordering relationship to other classes instances. The final size to oversample the clusters depends on their complexity and their initial size so that more synthetic instances are generated for more complex and smaller clusters while fewer instances are generated for less complex and larger clusters. As a secondary contribution, existing Oversampling methods for two-class classification have been extended for ordinal regression. Results demonstrate that the proposed CWOS-Ord method provides significantly better results compared to other methods based on the performance measures. A new Oversampling method for addressing ordinal regression with imbalanced datasets is presented.Minority classes are clustered by considering the instances of other classes.A new measurement is proposed to find the final size of clusters based on their complexity and initial size.Minority instances are oversampled based on their distances and ordering relationship to other classes instances.
Marcin Bownik - One of the best experts on this subject based on the ideXlab platform.
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Oversampling of wavelet frames for real dilations
Journal of the London Mathematical Society, 2012Co-Authors: Marcin Bownik, Jakob LemvigAbstract:We generalize the Second Oversampling Theorem for wavelet frames and dual wavelet frames from the setting of integer dilations to real dilations. We also study the relationship between dilation matrix Oversampling of semi-orthogonal Parseval wavelet frames and the additional shift invariance gain of the core subspace. Oversampling of wavelet frames has been a subject of extensive study by several authors dating back to the early 1990s. The first Oversampling results are due to Chui and Shi [16, 17], who proved that Oversampling by odd factors preserves tightness of dyadic affine frames. This is now the central result of the subject known as the Second Oversampling Theorem. Its higher dimensional generalizations to integer matrix dilations were studied by Chui and Shi [18], Johnson [27], Laugesen [30], and Ron and Shen [31]. In particular, these authors introduced (in several equivalent forms) the class of Oversampling matrices ‘relatively prime’ to a fixed dilation A and they established several Oversampling results for (not necessarily tight) affine frames. Dutkay and Jorgensen [23] shed a new light on these results by showing that Oversampling of orthonormal (or frame) wavelets by such matrices leads to orthonormal (or frame) superwavelets, respectively. Chui and Sun [22] have completed the understanding of the case of integer dilations by showing that the class of ‘relatively prime’ matrices is optimal for the Second Oversampling Theorem; that is, if an Oversampling matrix falls out of this class, then the Oversampling does not preserve a tight frame property in general. However, it is possible to give a characterization of Oversampling matrices preserving tightness once affine frame generators are chosen. These
