@inproceedings{53304,
  author       = {{Kuschel, Maurice and Hasija, Tanuj and Marrinan, Timothy}},
  booktitle    = {{ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  publisher    = {{IEEE}},
  title        = {{{Rademacher Complexity Regularization for Correlation-Based Multiview Representation Learning}}},
  doi          = {{10.1109/icassp48485.2024.10446173}},
  year         = {{2024}},
}

@article{53301,
  author       = {{Vieluf, Solveig and Hasija, Tanuj and Kuschel, Maurice and Reinsberger, Claus and Loddenkemper, Tobias}},
  issn         = {{0957-4174}},
  journal      = {{Expert Systems with Applications}},
  keywords     = {{Artificial Intelligence, Computer Science Applications, General Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Developing a deep canonical correlation-based technique for seizure prediction}}},
  doi          = {{10.1016/j.eswa.2023.120986}},
  volume       = {{234}},
  year         = {{2023}},
}

@inproceedings{53303,
  author       = {{Kuschel, Maurice and Marrinan, Timothy and Hasija, Tanuj}},
  booktitle    = {{2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)}},
  publisher    = {{IEEE}},
  title        = {{{Geodesic-Based Relaxation For Deep Canonical Correlation Analysis}}},
  doi          = {{10.1109/mlsp55844.2023.10285937}},
  year         = {{2023}},
}

@inproceedings{49825,
  author       = {{Lehmann, Isabell and Acar, Evrim and Hasija, Tanuj and Akhonda, M.A.B.S. and Calhoun, Vince D. and Schreier, Peter and Adali, Tulay}},
  booktitle    = {{ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  publisher    = {{IEEE}},
  title        = {{{Multi-Task fMRI Data Fusion Using IVA and PARAFAC2}}},
  doi          = {{10.1109/icassp43922.2022.9747662}},
  year         = {{2022}},
}

@inproceedings{40669,
  author       = {{Hasija, Tanuj and Marrinan, Timothy}},
  booktitle    = {{Proc. 30th European Signal Processing Conference (EUSIPCO)}},
  pages        = {{2091–2095}},
  title        = {{{A GLRT for estimating the number of correlated components in sample-poor mCCA}}},
  year         = {{2022}},
}

@inproceedings{40670,
  author       = {{Lehmann, Isabell and Acar, Evrim and Hasija, Tanuj and Akhonda, M.A.B.S. and Calhoun, Vince D. and Schreier, Peter J. and Adali, Tülay}},
  booktitle    = {{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  pages        = {{1466--1470}},
  title        = {{{Multi-Task fMRI Data Fusion Using IVA and PARAFAC2}}},
  doi          = {{10.1109/ICASSP43922.2022.9747662}},
  year         = {{2022}},
}

@article{37611,
  author       = {{Hasija, Tanuj and Marrinan, Timothy and Lameiro, Christian and Schreier, Peter J}},
  journal      = {{Signal Processing}},
  publisher    = {{Elsevier}},
  title        = {{{Determining the dimension and structure of the subspace correlated across multiple data sets}}},
  doi          = {{10.1016/j.sigpro.2020.107613}},
  volume       = {{176}},
  year         = {{2020}},
}

@article{31710,
  author       = {{Vieluf, S and Scheer, V and Hasija, Tanuj and Schreier, PJ and Reinsberger, Claus}},
  issn         = {{1543-8627}},
  journal      = {{Res Sports Med}},
  number       = {{2}},
  pages        = {{231--240}},
  title        = {{{Multimodal approach towards understanding the changes in the autonomic nervous system induced by an ultramarathon.}}},
  volume       = {{28}},
  year         = {{2020}},
}

@inproceedings{37612,
  author       = {{Hasija, Tanuj and Gölz, Martin and Muma, Michael and Schreier, Peter J and Zoubir, Abdelhak M}},
  booktitle    = {{Proc. Asilomar Conf. on Signals, Systems, Computers}},
  title        = {{{Source Enumeration and Robust Voice Activity Detection in Wireless Acoustic Sensor Networks}}},
  year         = {{2019}},
}

@inproceedings{40681,
  author       = {{Lameiro, Christian and Hasija, Tanuj and Marrinan, Tim and Schreier, Peter J}},
  booktitle    = {{ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  pages        = {{5152–5156}},
  title        = {{{Estimating the Number of Correlated Components Based on Random Projections}}},
  year         = {{2019}},
}

@article{31709,
  author       = {{Vieluf, S and Hasija, Tanuj and Jakobsmeyer, Rasmus and Schreier, PJ and Reinsberger, Claus}},
  issn         = {{1664-042x}},
  journal      = {{Front Physiol}},
  pages        = {{240}},
  title        = {{{Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network.}}},
  volume       = {{10}},
  year         = {{2019}},
}

@inproceedings{40702,
  author       = {{Marrinan, T. and Hasija, Tanuj and Lameiro, C. and Schreier, P. J.}},
  booktitle    = {{Proc. European Signal Process. Conf. (EUSIPCO)}},
  pages        = {{1082–1086}},
  title        = {{{Complete Model Selection in Multiset Canonical Correlation Analysis}}},
  year         = {{2018}},
}

@article{40711,
  abstract     = {{Abstract: A complex-valued signal is improper if it is correlated with its complex conjugate. The dimension of the improper signal subspace, i.e., the number of improper components in a complex-valued measurement, is an important parameter and is unknown in most of the applications. In this letter, we introduce two approaches to estimate this dimension: one based on an information-theoretic criterion and the other based on hypothesis testing. We also present reduced-rank versions of these approaches that work for scenarios where the number of observations is comparable to or even smaller than the dimension of the data. Unlike other techniques for determining model orders, our techniques also work in the presence of additive colored noise.}},
  author       = {{Hasija, Tanuj and Lameiro, Christian and Schreier, Peter J.}},
  journal      = {{IEEE Signal Process. Lett.}},
  pages        = {{1606–1610}},
  title        = {{{Determining the dimension of the improper signal subspace in complex-valued data}}},
  volume       = {{24}},
  year         = {{2017}},
}

@article{40720,
  author       = {{Song, Yang and Schreier, Peter J. and Ramírez, David and Hasija, Tanuj}},
  journal      = {{Signal Process.}},
  pages        = {{449–458}},
  title        = {{{Canonical correlation analysis of high-dimensional data with very small sample support}}},
  volume       = {{128}},
  year         = {{2016}},
}

@inproceedings{40723,
  author       = {{Hasija, Tanuj and Song, Yang and Schreier, Peter J. and Ramírez, David}},
  booktitle    = {{Proc.\ IEEE Work.\ Stat.\ Signal Process.}},
  title        = {{{Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime}}},
  year         = {{2016}},
}

@inproceedings{40718,
  author       = {{Hasija, Tanuj and Song, Yang and Schreier, Peter J. and Ramírez, David}},
  booktitle    = {{Proc.\ Asilomar Conf.\ Signals\ Syst. Computers}},
  title        = {{{Bootstrap-based detection of the number of signals correlated across multiple data sets}}},
  year         = {{2016}},
}

@inproceedings{40721,
  author       = {{Song, Yang and Hasija, Tanuj and Schreier, Peter J. and Ramírez, David}},
  booktitle    = {{Proc.\ Eur.\ Signal Process.\ Conf.}},
  title        = {{{Determining the number of signals correlated across multiple data sets for small sample support}}},
  year         = {{2016}},
}