Researchers have developed a new expectation-maximization (EM) algorithm called E$^2$M for tensor-based discrete density estimation. This algorithm addresses challenges with traditional $\alpha$-divergence methods by first minimizing a surrogate objective using Kullback-Leibler (KL) divergence, which allows for standard EM updates. It then employs a tensor many-body approximation in the M-step to achieve simultaneous closed-form parameter updates. The E$^2$M algorithm supports flexible modeling of various low-rank structures, including CP, Tucker, and Tensor Train formats, and has demonstrated comparable convergence to gradient-based methods, robustness to outliers, and superior density estimation performance on synthetic and real datasets. AI
IMPACT Introduces a novel algorithmic approach for density estimation that could improve performance in various machine learning applications.
RANK_REASON The cluster contains an academic paper detailing a new algorithm and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
- CP format
- E2M: Double Bounded α-Divergence Optimization for Tensor-based Discrete Density Estimation
- Expectation maximization (EM) algorithms using polar symmetries for computed tomography (CT) image reconstruction.
- Kazu Ghalamkari
- Kullback--Leibler (KL) divergence
- Tucker format
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