Gradient adaptation under unit-norm constraints

S. C. Douglas, S. Amari, S. Y. Kung

Research output: Contribution to conferencePaper

11 Scopus citations

Abstract

In this paper, we study gradient-based adaptive algorithms within parameter spaces specified by ∥w∥ = 1, where ∥ · ∥ is any vector norm. Several approximate algorithms for this task have already been developed when ∥w∥ is the L2 norm. We derive general algorithm forms for arbitrary vector norms and relate them to true gradient procedures via their geometric structures. We also give algorithms that mitigate an inherent numerical instability for tangent-vector L2-norm methods. Simulations showing the performance of the techniques for minor component analysis are provided.

Original languageEnglish (US)
Pages144-147
Number of pages4
StatePublished - Dec 1 1998
Externally publishedYes
EventProceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing - Portland, OR, USA
Duration: Sep 14 1998Sep 16 1998

Other

OtherProceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing
CityPortland, OR, USA
Period9/14/989/16/98

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Douglas, S. C., Amari, S., & Kung, S. Y. (1998). Gradient adaptation under unit-norm constraints. 144-147. Paper presented at Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing, Portland, OR, USA, .