The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Carlos Cabrelli - One of the best experts on this subject based on the ideXlab platform.
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time frequency shift invariance of gabor spaces generated by integer lattices
Journal of Mathematical Analysis and Applications, 2019Co-Authors: Ursula Molter, Carlos Cabrelli, Götz E. PfanderAbstract:Abstract We study extra time-frequency shift invariance properties of Gabor spaces. For a Gabor space generated by an integer lattice, we state and prove several characterizations for its time-frequency shift invariance with respect to a finer integer lattice. The extreme cases of full translation invariance, full modulation invariance, and full time-frequency shift invariance are also considered. The results show a close analogy with the extra translation invariance of shift-invariant spaces.
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time frequency shift invariance of gabor spaces generated by integer lattices
arXiv: Classical Analysis and ODEs, 2017Co-Authors: Carlos Cabrelli, Ursula Molter, Götz E. PfanderAbstract:We study extra time-frequency shift invariance properties of Gabor spaces. For a Gabor space generated by an integer lattice, we state and prove several characterizations for its time-frequency shift invariance with respect to a finer integer lattice. Some extreme cases are also considered. The result obtained shows a close analogy with the extra translation invariance of shift-invariant spaces, however, presents subtle but deep differences, due to the non-commutativity of the time-frequency operations.
Eamonn J. Keogh - One of the best experts on this subject based on the ideXlab platform.
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CID: an efficient complexity-invariant distance for time series
Data Mining and Knowledge Discovery, 2014Co-Authors: Gustavo E A P A Batista, Eamonn J. Keogh, Oben Moses Tataw, Vinícius M. A. SouzaAbstract:The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.
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A Complexity-Invariant Distance Measure for Time Series
Proceedings of the 2011 SIAM International Conference on Data Mining, 2011Co-Authors: Gustavo E A P A Batista, Xiaoyue Wang, Eamonn J. KeoghAbstract:The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While there is a plethora of classification algorithms that can be applied to time series, all of the current empirical evidence suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping. In this work we make a surprising claim. There is an invariance that the community has missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where complex objects are incorrectly assigned to a simpler class. We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series classification experiments ever attempted, and show that complexity-invariant distance measures can produce improvements in accuracy in the vast majority of cases. Copyright © SIAM.
Götz E. Pfander - One of the best experts on this subject based on the ideXlab platform.
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time frequency shift invariance of gabor spaces generated by integer lattices
Journal of Mathematical Analysis and Applications, 2019Co-Authors: Ursula Molter, Carlos Cabrelli, Götz E. PfanderAbstract:Abstract We study extra time-frequency shift invariance properties of Gabor spaces. For a Gabor space generated by an integer lattice, we state and prove several characterizations for its time-frequency shift invariance with respect to a finer integer lattice. The extreme cases of full translation invariance, full modulation invariance, and full time-frequency shift invariance are also considered. The results show a close analogy with the extra translation invariance of shift-invariant spaces.
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time frequency shift invariance of gabor spaces generated by integer lattices
arXiv: Classical Analysis and ODEs, 2017Co-Authors: Carlos Cabrelli, Ursula Molter, Götz E. PfanderAbstract:We study extra time-frequency shift invariance properties of Gabor spaces. For a Gabor space generated by an integer lattice, we state and prove several characterizations for its time-frequency shift invariance with respect to a finer integer lattice. Some extreme cases are also considered. The result obtained shows a close analogy with the extra translation invariance of shift-invariant spaces, however, presents subtle but deep differences, due to the non-commutativity of the time-frequency operations.
Gustavo E A P A Batista - One of the best experts on this subject based on the ideXlab platform.
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CID: an efficient complexity-invariant distance for time series
Data Mining and Knowledge Discovery, 2014Co-Authors: Gustavo E A P A Batista, Eamonn J. Keogh, Oben Moses Tataw, Vinícius M. A. SouzaAbstract:The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.
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A Complexity-Invariant Distance Measure for Time Series
Proceedings of the 2011 SIAM International Conference on Data Mining, 2011Co-Authors: Gustavo E A P A Batista, Xiaoyue Wang, Eamonn J. KeoghAbstract:The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While there is a plethora of classification algorithms that can be applied to time series, all of the current empirical evidence suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping. In this work we make a surprising claim. There is an invariance that the community has missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where complex objects are incorrectly assigned to a simpler class. We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series classification experiments ever attempted, and show that complexity-invariant distance measures can produce improvements in accuracy in the vast majority of cases. Copyright © SIAM.
Ursula Molter - One of the best experts on this subject based on the ideXlab platform.
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time frequency shift invariance of gabor spaces generated by integer lattices
Journal of Mathematical Analysis and Applications, 2019Co-Authors: Ursula Molter, Carlos Cabrelli, Götz E. PfanderAbstract:Abstract We study extra time-frequency shift invariance properties of Gabor spaces. For a Gabor space generated by an integer lattice, we state and prove several characterizations for its time-frequency shift invariance with respect to a finer integer lattice. The extreme cases of full translation invariance, full modulation invariance, and full time-frequency shift invariance are also considered. The results show a close analogy with the extra translation invariance of shift-invariant spaces.
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time frequency shift invariance of gabor spaces generated by integer lattices
arXiv: Classical Analysis and ODEs, 2017Co-Authors: Carlos Cabrelli, Ursula Molter, Götz E. PfanderAbstract:We study extra time-frequency shift invariance properties of Gabor spaces. For a Gabor space generated by an integer lattice, we state and prove several characterizations for its time-frequency shift invariance with respect to a finer integer lattice. Some extreme cases are also considered. The result obtained shows a close analogy with the extra translation invariance of shift-invariant spaces, however, presents subtle but deep differences, due to the non-commutativity of the time-frequency operations.