The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.
Tensor Decomposition for Signal Processing and Machine Learning
Open accessAbstract
Tensors or multiway arrays are functions of three or more indices (i, j, k, . . . )-similar to matrices (two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recently become ubiquitous in signal and data analytics at the confluence of signal processing, statistics, data mining, and machine learning. This overview article aims to provide a good starting point for researchers and practitioners interested in learning about and working with tensors. As such, it focuses on fundamentals and motivation (using various application examples), aiming to strike an appropriate balance of breadth and depth that will enable someone having taken first graduate courses in matrix algebra and probability to get started doing research and/or developing tensor algorithms and software. Some background in applied optimization is useful but not strictly required. The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties (including fairly good coverage of identifiability); broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from source separation to collaborative filtering, mixture and topic modeling, classification, and multilinear subspace learning.
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Neural Architecture Search for Transformers: A Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3212767 · Cited by 118
Lightspeech: Lightweight and Fast Text to Speech with Neural Architecture Search
Luo, R., Tan, X., Wang, R., Qin, T., Li, J., Zhao, S., Chen, E., & Liu, T.-Y. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9414403 · Cited by 67
Falaschetti, L., Manoni, L., Rivera, R. C. F., Pau, D., Romanazzi, G., Silvestroni, O., Tomaselli, V., & Turchetti, C. (2021) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2021.3098454 · Cited by 45
Low dimensional subject representation based transfer learning in eeg decoding
Jeng, P.-Y., Wei, C.-S., Jung, T.-P., & Wang, L.-C. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3025865 · Cited by 35
Metadata
- Authors
- Sidiropoulos, N. D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E. E., & Faloutsos, C.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2017-07-01
- Volume
- 65
- Issue
- 13
- Pages
- 3551-3582
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2469230926
- Cited by
- 1,617
- References
- 106
- FWCI
- 53.94
- Type
- journal article
- Language
- en
- Open access
- green
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- Sidiropoulos, N. D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E. E., & Faloutsos, C. (2017). Tensor Decomposition for Signal Processing and Machine Learning. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2017.2690524
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