5 accepted papers
This paper introduces TenSOFO, a novel tensor-based method specifically designed for blind source separation (BSS). Ten-SOFO presents a new efficient alternating direction method of multipliers framework, allowing for simultaneous decomposition of two symmetric third-order tensors under the individu…
In this paper, we investigate the problem of convolutive blind source separation (BSS) via tensor decomposition. A fundamental link between convolutive BSS and block-term decomposition (BTD) is established, forming the basis for our novel tensor-based convolutive BSS method, namely TCBSS. Specifical…
We studied the problem of robust subspace tracking (RST) in contaminated environments. Leveraging the fast approximated power iteration and α-divergence, a novel robust algorithm called αFAPI was developed for tracking the underlying principal subspace of streaming data over time. αFAPI is fast and…
We studied the problem of sparse subspace tracking in the high-dimensional regime where the dimension is comparable to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm called OPIT was derived for tracking the sparse principal subspace…
In this paper, we introduce a fast adaptive algorithm for CAN- DECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator t…