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17 Publications
2024 | Conference Paper | LibreCat-ID: 53304
M. Kuschel, T. Hasija, and T. Marrinan, “Rademacher Complexity Regularization for Correlation-Based Multiview Representation Learning,” 2024, doi: 10.1109/icassp48485.2024.10446173.
LibreCat
| DOI
2023 | Journal Article | LibreCat-ID: 53301
S. Vieluf, T. Hasija, M. Kuschel, C. Reinsberger, and T. Loddenkemper, “Developing a deep canonical correlation-based technique for seizure prediction,” Expert Systems with Applications, vol. 234, Art. no. 120986, 2023, doi: 10.1016/j.eswa.2023.120986.
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 53303
M. Kuschel, T. Marrinan, and T. Hasija, “Geodesic-Based Relaxation For Deep Canonical Correlation Analysis,” 2023, doi: 10.1109/mlsp55844.2023.10285937.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 49825
I. Lehmann et al., “Multi-Task fMRI Data Fusion Using IVA and PARAFAC2,” 2022, doi: 10.1109/icassp43922.2022.9747662.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 40669
T. Hasija and T. Marrinan, “A GLRT for estimating the number of correlated components in sample-poor mCCA,” in Proc. 30th European Signal Processing Conference (EUSIPCO), 2022, pp. 2091–2095.
LibreCat
2022 | Conference Paper | LibreCat-ID: 40670
I. Lehmann et al., “Multi-Task fMRI Data Fusion Using IVA and PARAFAC2,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1466–1470, doi: 10.1109/ICASSP43922.2022.9747662.
LibreCat
| DOI
2020 | Journal Article | LibreCat-ID: 37611
T. Hasija, T. Marrinan, C. Lameiro, and P. J. Schreier, “Determining the dimension and structure of the subspace correlated across multiple data sets,” Signal Processing, vol. 176, 2020, doi: 10.1016/j.sigpro.2020.107613.
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| DOI
2020 | Journal Article | LibreCat-ID: 31710
S. Vieluf, V. Scheer, T. Hasija, P. Schreier, and C. Reinsberger, “Multimodal approach towards understanding the changes in the autonomic nervous system induced by an ultramarathon.,” Res Sports Med, vol. 28, no. 2, pp. 231–240, 2020.
LibreCat
| PubMed | Europe PMC
2019 | Conference Paper | LibreCat-ID: 37612
T. Hasija, M. Gölz, M. Muma, P. J. Schreier, and A. M. Zoubir, “Source Enumeration and Robust Voice Activity Detection in Wireless Acoustic Sensor Networks,” 2019.
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2019 | Conference Paper | LibreCat-ID: 40681
C. Lameiro, T. Hasija, T. Marrinan, and P. J. Schreier, “Estimating the Number of Correlated Components Based on Random Projections,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 5152–5156.
LibreCat
2019 | Journal Article | LibreCat-ID: 31709
S. Vieluf, T. Hasija, R. Jakobsmeyer, P. Schreier, and C. Reinsberger, “Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network.,” Front Physiol, vol. 10, p. 240, 2019.
LibreCat
| PubMed | Europe PMC
2018 | Conference Paper | LibreCat-ID: 40702
T. Marrinan, T. Hasija, C. Lameiro, and P. J. Schreier, “Complete Model Selection in Multiset Canonical Correlation Analysis,” in Proc. European Signal Process. Conf. (EUSIPCO), 2018, pp. 1082–1086.
LibreCat
2017 | Journal Article | LibreCat-ID: 40711
T. Hasija, C. Lameiro, and P. J. Schreier, “Determining the dimension of the improper signal subspace in complex-valued data,” IEEE Signal Process. Lett., vol. 24, pp. 1606–1610, 2017.
LibreCat
2016 | Journal Article | LibreCat-ID: 40720
Y. Song, P. J. Schreier, D. Ramírez, and T. Hasija, “Canonical correlation analysis of high-dimensional data with very small sample support,” Signal Process., vol. 128, pp. 449–458, 2016.
LibreCat
2016 | Conference Paper | LibreCat-ID: 40723
T. Hasija, Y. Song, P. J. Schreier, and D. Ramírez, “Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime,” 2016.
LibreCat
2016 | Conference Paper | LibreCat-ID: 40718
T. Hasija, Y. Song, P. J. Schreier, and D. Ramírez, “Bootstrap-based detection of the number of signals correlated across multiple data sets,” 2016.
LibreCat
2016 | Conference Paper | LibreCat-ID: 40721
Y. Song, T. Hasija, P. J. Schreier, and D. Ramírez, “Determining the number of signals correlated across multiple data sets for small sample support,” 2016.
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