Abstract:
Matrix decomposition is essential for mining big data from modern applications. However, processing very large data on a single machine is inefficient or impossible. Big data are also distributed across multiple machines.
Thus, heterogeneous noise dominates such data. Big data analytics needs distributed matrix decomposition. A scalable method should model heterogeneous noise and solve distributed system communication problems.
We propose a distributed Bayesian matrix decomposition model (DBMD) for big data mining and clustering. We implement distributed computing using three methods: accelerated gradient descent, ADMM, and statistical inference.
We study these algorithms’ theoretical convergence. An optimal plug-in weighted average reduces estimation variance to address noise heterogeneity. Our algorithms scale well to big data and outperform Scalable-NMF and scalable k-means++, two typical distributed methods.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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