Create README.md

Author: thanhtbt

README.md

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+# TT: Tracking Online Low-Rank Approximations of Higher-Order Incomplete Streaming Tensors
+
+We propose two new provable algorithms for tracking online low-rank approximations of higher-order streaming tensors in the presence of missing data. The first algorithm, dubbed adaptive CP
+decomposition (ACP), minimizes an exponentially weighted recursive least-squares cost function to obtain the tensor factors in an efficient way, thanks to the alternative minimization framework and the randomized
+sketching technique. Under the Tucker model, the second algorithm called adaptive Tucker decomposition (ATD), which is more flexible than the first one, first tracks the underlying low-dimensional subspaces covering
+the tensor factors, and then estimates the core tensor using a stochastic approximation. 
+
+Both algorithms are fast, and require a low computational complexity and memory storage.
+
+
+## DEMO
+
++ Run files "demo_ACP_xyz.m" and "demo_ATD_xyz" for synthetic data.
++ Run "demo_real_video_tracking_completion.m" for a practical applications of online tensor completion. Video datasets can be downloaded from Releases 
+
+
+## References
+
+This code is free and open source for research purposes. If you use this code, please acknowledge the following papers.
+
+[1] L.T. Thanh, K. Abed-Meraim, N. L. Trung and A. Hafiane. “[*A Fast Randomized Adaptive CP Decomposition For Streaming Tensors*](https://drive.google.comg)”. **Proceedings of the 46th IEEE ICASSP**. [[DOI](https://ieeexplore.ieee.org/document/9413554)].
+
+[2] L.T. Thanh, K. Abed-Meraim, N. L. Trung and A. Hafiane. “[*Tracking Online Low-Rank Approximations of Higher-Order Incomplete Streaming Tensors*](https://drive.google.com/fi)”. **ACM Journal** (submitted).