Abstract:
Data mining tasks have used nonnegative matrix factorization (NMF). Due to its high cost on large matrices, NMF acceleration is gaining popularity.
However, since NMF is widely used in image and text analysis, it may use privacy data (e.g., medical images and records) from multiple parties (e.g., hospitals). This paper examines distributed NMF acceleration and security.
First, we propose a distributed sketched alternating nonnegative least squares (DSANLS) framework for NMF that uses matrix sketching to reduce subproblem size with a convergence guarantee.
For the second problem, DSANLS with modification can be adapted to the security setting for one or a few iterations. Thus, we propose four secure, efficient distributed NMF methods in synchronous and asynchronous settings. Our methods are tested on several real datasets.
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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