General Data Analytics with Applications to Visual Information Analysis: A Provable Backward-Compatible Semisimple Paradigm over T-Algebra

• URL: http://arxiv.org/abs/2011.00307v8
• Date: Sun, 2 May 2021 15:45:32 GMT
• Title: General Data Analytics with Applications to Visual Information Analysis: A Provable Backward-Compatible Semisimple Paradigm over T-Algebra
• Authors: Liang Liao and Stephen John Maybank
• Abstract summary: We consider a novel backward-compatible paradigm of general data analytics over a recently-reported semisimple algebra. We generalize some canonical algorithms for visual pattern analysis. Experiments on public datasets show that the generalized algorithms compare favorably with their canonical counterparts.
• Score: 7.028302194243312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider a novel backward-compatible paradigm of general data analytics over a recently-reported semisimple algebra (called t-algebra). We study the abstract algebraic framework over the t-algebra by representing the elements of t-algebra by fix-sized multi-way arrays of complex numbers and the algebraic structure over the t-algebra by a collection of direct-product constituents. Over the t-algebra, many algorithms are generalized in a straightforward manner using this new semisimple paradigm. To demonstrate the new paradigm's performance and its backward-compatibility, we generalize some canonical algorithms for visual pattern analysis. Experiments on public datasets show that the generalized algorithms compare favorably with their canonical counterparts.

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