Polar $n$-Complex and $n$-Bicomplex Singular Value Decomposition and Principal Component Pursuit

Abstract

Informed by recent work on tensor singular value decomposition and circulant algebra matrices, this paper presents a new theoretical bridge that unifies the hypercomplex and tensor-based approaches to singular value decomposition and robust principal component analysis. We begin our work by extending the principal component pursuit to Olariu's polar $n$-complex numbers as well as their bicomplex counterparts. In so doing, we have derived the polar $n$-complex and $n$-bicomplex proximity operators for both the ${\ell }_1$- and trace-norm regularizers, which can be used by proximal optimization methods such as the alternating direction method of multipliers. Experimental results on two sets of audio data show that our algebraically-informed formulation outperforms tensor robust principal component analysis. We conclude with the message that an informed definition of the trace norm can bridge the gap between the hypercomplex and tensor-based approaches. Our approach can be seen as a general methodology for generating other principal component pursuit algorithms with proper algebraic structures.

12 pages, 2 figures

References in corpus (4)

• The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
• Tensor-based formulation and nuclear norm regularization for multi-energy computed tomography
• Complex Numbers in n Dimensions
• Complex and Quaternionic Principal Component Pursuit and Its Application to Audio Separation

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