T.-S. T. Chan and A. Gibberd, “Feasible model-based principal component analysis: Joint estimation of rank and error covariance matrix,” Comput. Stat. Data Anal., vol. 201, no. 108042, Jan. 2025.
L. Mosley, T.-S. T. Chan and A. Gibberd, “The sparse dynamic factor model: A regularised quasi-maximum likelihood approach,” Stat. Comput., vol. 34, no. 68, Jan. 2024.
A. Mahdi, P. Błaszczyk, P. Dłotko, D. Salvi, T.-S. Chan, J. Harvey, D. Gurnari, Y. Wu, A. Farhat, N. Hellmer, A. Zarebski, B. Hogan, and L. Tarassenko, “OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19,” Sci. Rep., vol. 11, no. 9237, Apr. 2021.
Z.-C. Fan, T.-S. T. Chan, Y.-H. Yang, and J.-S. R. Jang, “Backpropagation with N-D vector-valued neurons using arbitrary bilinear products,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 7, pp. 2638–2652, Jul. 2020.
T.-S. T. Chan and Y.-H. Yang, “Informed group-sparse representation for singing voice separation,” IEEE Signal Process. Lett., vol. 24, no. 2, pp. 156–160, Feb. 2017.
T.-S. T. Chan and Y.-H. Yang, “Polar n-complex and n-bicomplex singular value decomposition and principal component pursuit,” IEEE Trans. Signal Process., vol. 64, no. 24, pp. 6533–6544, Dec. 2016.
T.-S. T. Chan and Y.-H. Yang, “Complex and quaternionic principal component pursuit and its application to audio separation,” IEEE Signal Process. Lett., vol. 23, no. 2, pp. 287–291, Feb. 2016.
A. Kumar and T.-S. T. Chan, “Robust ear identification using sparse representation of local texture descriptors,” Pattern Recognition, vol. 46, no. 1, pp. 73–85, Jan. 2013.
T.-S. Chan and A. Kumar, “Reliable ear identification using 2-D quadrature filters,” Pattern Recognition Lett., vol. 33, no. 14, pp. 1870–1881, Oct. 2012.
B. Mitchinson, T.-S. Chan, J. Chambers, M. Pearson, M. Humphries, C. Fox, K. Gurney, and T. J. Prescott, “BRAHMS: Novel middleware for integrated systems computation,” Advanced Eng. Informatics, vol. 24, no. 1, pp. 49–61, Jan. 2010.
T.-S. T. Chan and A. Gibberd, “Identifying metering hierarchies with distance correlation and dominance constraints,” in Proc. IEEE Int. Conf. Mach. Learn. Appl., 2022, pp. 1551–1558.
Z.-C. Fan, T.-S. Chan, Y.-H. Yang, and J.-S. R. Jang, “Deep cyclic group networks,” in Proc. Int. Joint Conf. Neural Netw., 2019, pp. 1–8.
C.-A. Yu, C.-L. Tai, T.-S. Chan, and Y.-H. Yang, “Modeling multi-way relations with hypergraph embedding,” in Proc. Int. Conf. Inform. Knowledge Manage., 2018, pp. 1707–1710.
C.-A. Yu, T.-S. Chan, and Y.-H. Yang, “Low-rank matrix completion over finite Abelian group algebras for context-aware recommendation,” in Proc. Int. Conf. Inform. Knowledge Manage., 2017, pp. 2415–2418.
Z.-C. Fan, T.-S. T. Chan, Y.-H. Yang, and J.-S. R. Jang, “Music signal processing using vector product neural networks,” in Proc. IJCNN Workshop Deep Learn. Music, 2017, pp. 26–30.
T.-S. Chan, T.-C. Yeh, Z.-C. Fan, H.-W. Chen, L. Su, Y.-H. Yang, and R. Jang, “Vocal activity informed singing voice separation with the iKala dataset,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2015, pp. 718–722.
A. Kumar and T.-S. Chan, “Iris recognition using quaternionic sparse orientation code (QSOC),” in Proc. CVPR Workshop Biometrics, 2012, pp. 59–64.
A. Kumar, T.-S. Chan, and C.-W. Tan, “Human identification from at-a-distance face images using sparse representation of local iris features,” in Proc. Int. Conf. Biometrics, 2012, pp. 303–309.
T.-S. T. Chan and G. A. Wiggins, “More evidence for a computational memetics approach to music information and new interpretations of an aesthetic fitness measure,” in Proc. ECAI Workshop Comput. Creativity, 2006, pp. 13–17.
T.-S. T. Chan and G. A. Wiggins, “A computational memetics approach to music information and aesthetic fitness,” in Proc. IJCAI Workshop Comput. Creativity, 2005, pp. 105–108.
T.-S. Chan, X. Tian, K. Zheng, and A. Gibberd*, “Exploring dynamic factors of fMRI activity in the presence of sparse loadings,” presented at Int. Conf. ERCIM WG Comput. Methodol. Stat., Berlin, Germany, 2023.
T. Chan, “Hypercomplex and informed source separation for machine listening and brain-computer music interfacing,” presented at FWF-MOST Joint Seminar Culture- Location-Aware Music Recommendation Retrieval, Taipei, Taiwan, 2016.
B. Mitchinson*, T. Chan, J. Chambers, M. Humphries, K. Gurney, and T. Prescott, “BRAHMS: Novel middleware for integrated systems computation,” presented at INCF Congr. Neuroinformatics, Stockholm, Sweden, 2008.
Wookey and T.-S., “Porting the Linux kernel to a new ARM platform,” Wireless Solutions J., vol. 4, pp. 52–59, Summer 2002.
T.-S. T. Chan, “A cognitive information theory of music: A computational memetics approach,” Ph.D. dissertation, Univ. London, London, UK, 2008. (errata)