Kejun Huang – Publications

  • K. Huang and X. Fu, “Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm”, in International Conference on Machine Learning (ICML), 2019, Long Beach, CA. [doi]

  • K. Huang, Z. Yang, Z. Wang, and M. Hong, “Learning Partially Observable Markov Decision Processes using Coupled Canonical Polyadic Decomposition”, in IEEE Data Science Workshop (DSW), 2019, Minneapolis, MN. [doi]

  • S. Lu, Z. Zhao, K. Huang, and M. Hong, “Perturbed Projected Gradient Descent Converges to Approximate Second-Order Points for Bound Constrained Nonconvex Problems”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019, Brighton, UK. [doi]

  • X. Fu, C. Gao, H.-T. Wai, and K. Huang, “Block-Randomized Stochastic Proximal Gradient for Constrained Low-Rank Tensor Factorization”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019, Brighton, UK. [doi]

  • X. Fu*, K. Huang*, N. D. Sidiropoulos, Q. Shi, and M. Hong, “Anchor-Free Correlated Topic Modeling”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(5):1056–1071, 2019. [doi]

    • Part of the results appears in
      K. Huang*, X. Fu*, and N. D. Sidiropoulos, “Anchor-free Correlated Topic Modeling: Identifiability and Algorithm”, in Advances in Neural Information Processing Systems (NIPS), 2016, Barcelona, Spain. [doi]

  • X. Fu, K. Huang, N. D. Sidiropoulos, and W.-K. Ma, “Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications”, IEEE Signal Processing Magazine, 36(2):59–80, 2019. [doi]

  • K. Huang, X. Fu, and N. D. Sidiropoulos, “Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling”, in International Conference on Machine Learning (ICML), 2018, Stockholm, Sweden. [doi]

  • S. Smith*, K. Huang*, N. D. Sidiropoulos, and G. Karypis, “Streaming Tensor Factorization for Infinite Data Sources”, in SIAM International Conference on Data Mining (SDM), 2018, San Diego, CA. [doi]

  • X. Fu*, K. Huang*, and N. D. Sidiropoulos, “On Identifiability of Nonnegative Matrix Factorization”, IEEE Signal Processing Letters, 25(3): 328–332, 2018. [doi]

  • K. Huang, X. Fu, and N. D. Sidiropoulos, “On Convergence of Epanechnikov Mean Shift”, in AAAI Conference on Artificial Intelligence (AAAI), 2018, New Orleans, LA. [doi]

  • A. P. Liavas, G. Kostouloas, G. Lourakis, K. Huang, and N. D. Sidiropoulos, “Nesterov-based Alternating Optimization for Nonnegative Tensor Factorization: Algorithm and Parallel Implementations”, IEEE Transactions on Signal Processing, 66(4): 944–953, 2018 [doi]

    • Part of the results appears in
      A. P. Liavas, G. Kostouloas, G. Lourakis, K. Huang, and N. D. Sidiropoulos, “Nesterov-based Parallel Algorithm for Large-scale Nonnegative Tensor Factorization”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017, New Orleans, LA. [doi]

  • K. Huang and N. D. Sidiropoulos, “Kullback-Leibler Principal Component for Tensors is not NP-hard”, in Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2017, Pacific Grove, CA. doi

  • X. Fu, K. Huang, O. Stretcu, H. Song, E. E. Papalexakis, P. P. Talukdar, T. Mitchell, N. D. Sidiropoulos, C. Faloutsos, and B. Pozcos, “BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals” in SIAM International Conference on Data Mining (SDM), 2017, Houston, TX.

  • K. Huang and Y. C. Eldar, “Phase Retrieval using a Conjugate Symmetric Reference”, in International Conference on Sampling Theory and Applications (SampTA), 2017, Tallinn, Estonia.

  • X. Fu, K. Huang, M. Hong, N. D. Sidiropoulos, and A. M. C. So, “Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis”, IEEE Transactions on Signal Processing, 65(16):4150–4165, 2017.

    • Part of the results appears in
      X. Fu, K. Huang, M. Hong, N. D. Sidiropoulos, and A. M. C. So, “Scalable and Flexible MAX-VAR Generalized Canonical Correlation Analysis via Alternating Optimization”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, New Orleans, LA.

  • N. D. Sidiropoulos, L. De Lathauwer, X. Fu, K. Huang, E. E. Papalexakis, and C. Faloutsos, “Tensor Decomposition for Signal Processing and Machine Learning”, IEEE Transactions on Signal Processing, 65(13): 3551–3582, 2017.

  • X. Fu, K. Huang, E. E. Papalexakis, H. Song, P. P. Talukdar, N. D. Sidiropoulos, C. Faloutsos, and T. Mitchell, “Efficient and Distributed Algorithms for Large-Scale Generalized Canonical Correlations Analysis”, in IEEE International Conference on Data Mining (ICDM), 2016, Barcelona, Spain.

  • X. Fu, K. Huang, B. Yang, W.-K. Ma, and N. D. Sidiropoulos, “Robust Volume Minimization-based Matrix Factorization for Remote Sensing and Document Clustering”, IEEE Transactions on Signal Processing, 64(23): 6254–6268, 2016.

    • Part of the results appears in
      X. Fu, W.-K. Ma, K. Huang, and N. D. Sidiropoulos, “Robust Volume Minimization-based Matrix Factorization via Alternating Optimization”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, Shanghai, China.

  • K. Huang, Y. C. Eldar, and N. D. Sidiropoulos, “Phase Retrieval from 1D Fourier Measurements: Convexity, Uniqueness, and Algorithms”, IEEE Transactions on Signal Processing, 64(23): 6105–6117, 2016.

    • Part of the results appears in
      K. Huang, Y. C. Eldar, and N. D. Sidiropoulos, “On Convexity and Identifiability in 1-D Fourier Phase Retrieval”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, Shanghai, China.

  • K. Huang and N. D. Sidiropoulos, “Consensus-ADMM for General Quadratically Constrained Quadratic Programming”, IEEE Transactions on Signal Processing, 64(20): 5297–5310, 2016.

  • C. Qian, N. D. Sidiropoulos, K. Huang, L. Huang, and H.-C. So, “Phase Retrieval Using Feasible Point Pursuit: Algorithms and Cramer-Rao Bound”, IEEE Transactions on Signal Processing, 64(20): 5282–5296, 2016.

    • Part of the results appears in
      C. Qian, N. D. Sidiropoulos, K. Huang, L. Huang, and H.-C. So, “Least Squares Phase Retrieval Using Feasible Point Pursuit”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, Shanghai, China.

  • K. Huang, N. D. Sidiropoulos, and A. P. Liavas, “A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization”, IEEE Transactions on Signal Processing, 64(19): 5052–5065, 2016.

    • Part of the results appears in
      K. Huang, N. D. Sidiropoulos, and A. P. Liavas, “Efficient Algorithms for ‘Universally’ Constrained Matrix and Tensor Factorization”, in European Signal Processing Conference (EUSIPCO), 2015, Nice, France.

  • X. Fu, K. Huang, W.-K. Ma, N. D. Sidiropoulos, and R. Bro, “Joint Tensor Factorization and Outlying Slab Suppression with Applications”, IEEE Transactions on Signal Processing, 63(23): 6315–6328, 2015.

  • M. Gardner*, K. Huang*, E. E. Papalexakis, X. Fu, P. P. Talukdar, C. Faloutsos, N. D. Sidiropoulos, and T. Mitchell, “Translation Invariant Word Embeddings”, in Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015, Lisbon, Portugal.

  • O. Mehanna, K. Huang, B. Gopalakrishnan, A. Konar, and N. D. Sidiropoulos, “Feasible Point Pursuit and Successive Approximation of Non-convex QCQPs”, IEEE Signal Processing Letters, 22(7): 804–808, 2015.

  • X. Fu, W.-K. Ma, K. Huang, and N. D. Sidiropoulos, “Blind Separation of Quasi-stationary Sources: Exploiting Convex Geometry in Covariance Domain”, IEEE Transactions on Signal Processing, 63(9): 2306–2320, 2015.

  • K. Huang, N. D. Sidiropoulos, E. E. Papalexakis, C. Faloutsos, P. P. Talukdar, and T. Mitchell, “Principled Neuro-Functional Connectivity Discovery”, in SIAM International Conference on Data Mining (SDM), 2015, Vancouver, Canada.

  • K. Huang and N. D. Sidiropoulos, “Putting NMF to the Test: A Tutorial Derivation of Pertinent Cramer-Rao Bounds and Performance Benchmarking”, IEEE Signal Processing Magazine, 31(3):76–86, 2014.

  • K. Huang, N. D. Sidiropoulos, and A. Swami, “Nonnegative Matrix Factorization Revisited: Uniqueness and Algorithm for Symmetric Decomposition”, IEEE Transactions on Signal Processing, 62(1): 211–224, 2014.

    • Part of the results appears in
      K. Huang, N. D. Sidiropoulos, and A. Swami, “NMF Revisited: New Uniqueness Results and Algorithms”, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013, Vancouver, Canada.