| Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering K Luong, R Nayak, T Balasubramaniam, MA Bashar Pattern Recognition 131, 108815, 2022 | 39 | 2022 |
| A novel approach to learning consensus and complementary information for multi-view data clustering K Luong, R Nayak 2020 IEEE 36th International Conference on Data Engineering (ICDE), 865-876, 2020 | 32 | 2020 |
| Progressive domain adaptation for detecting hate speech on social media with small training set and its application to COVID-19 concerned posts MA Bashar, R Nayak, K Luong, T Balasubramaniam Social Network Analysis and Mining 11 (1), 69, 2021 | 25 | 2021 |
| Identifying covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using nonnegative coupled matrix tensor factorization T Balasubramaniam, R Nayak, K Luong, MA Bashar Social Network Analysis and Mining 11 (1), 57, 2021 | 20 | 2021 |
| Learning inter-and intra-manifolds for matrix factorization-based multi-aspect data clustering K Luong, R Nayak IEEE Transactions on Knowledge and Data Engineering 34 (7), 3349-3362, 2020 | 16 | 2020 |
| Learning association relationship and accurate geometric structures for multi-type relational data K Luong, R Nayak 2018 IEEE 34th International Conference on Data Engineering (ICDE), 509-520, 2018 | 15 | 2018 |
| A novel technique of using coupled matrix and greedy coordinate descent for multi-view data representation K Luong, T Balasubramaniam, R Nayak Web Information Systems Engineering–WISE 2018: 19th International Conference …, 2018 | 14 | 2018 |
| Clustering multi-view data using non-negative matrix factorization and manifold learning for effective understanding: A survey paper K Luong, R Nayak Linking and Mining Heterogeneous and Multi-view Data, 201-227, 2019 | 12 | 2019 |
| Multi-type relational data clustering for community detection by exploiting content and structure information in social networks TM Gayani Tennakoon, K Luong, W Mohotti, S Chakravarthy, R Nayak PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim …, 2019 | 7 | 2019 |
| Clustering methods for multi-aspect data KTN Luong Queensland University of Technology, 2019 | 2 | 2019 |
| DCCNMF: Deep Complementary and Consensus Non-negative Matrix Factorization for multi-view clustering S Gunawardena, K Luong, T Balasubramaniam, R Nayak Knowledge-Based Systems 285, 111330, 2024 | 1 | 2024 |
| Multi-aspect Learning: Methods and Applications R Nayak, K Luong Springer Nature, 2023 | 1 | 2023 |
| Learning Consensus and Complementary Information for Multi-aspect Data Clustering R Nayak, K Luong Multi-aspect Learning: Methods and Applications, 127-150, 2023 | 1 | 2023 |
| Non-negative Matrix Factorization-Based Multi-aspect Data Clustering R Nayak, K Luong Multi-aspect Learning: Methods and Applications, 27-50, 2023 | | 2023 |
| Multi-aspect Data Learning: Overview, Challenges and Approaches R Nayak, K Luong Multi-aspect Learning: Methods and Applications, 1-25, 2023 | | 2023 |
| Spectral Clustering on Multi-aspect Data R Nayak, K Luong Multi-aspect Learning: Methods and Applications, 103-126, 2023 | | 2023 |
| Deep Learning-Based Methods for Multi-aspect Data Clustering R Nayak, K Luong Multi-aspect Learning: Methods and Applications, 151-184, 2023 | | 2023 |
| NMF and Manifold Learning for Multi-aspect Data R Nayak, K Luong Multi-aspect Learning: Methods and Applications, 51-76, 2023 | | 2023 |
| Subspace Learning for Multi-aspect Data R Nayak, K Luong Multi-aspect Learning: Methods and Applications, 77-101, 2023 | | 2023 |
| Multi-aspect Learning R Nayak, K Luong | | |
Richi NayakProfessor, Queensland University of TechnologyVerified email at qut.edu.au
