ABSTRACT
In this work, we extended the recently developed tensor decomposition (TD) based unsupervised feature extraction (FE) to a kernel based method, through a mathematical formulation. Subsequently, the kernel TD (KTD) based unsupervised FE was applied to two synthetic examples as well as real data sets, and the relevant findings were compared with those obtained previously using the TD based unsupervised FE approaches. The KTD based unsupervised FE demonstrated the most competitive performance against the TD based unsupervised FE in large p small n situations, involving a limited number of samples with many variables (observations). Nevertheless, the KTD based unsupervised FE outperformed the TD based unsupervised FE in non large p small n situations. In general, although the use of the kernel trick can help the TD based unsupervised FE gain more variations, a wider range of problems may also be encountered. Considering the comparable performance of the KTD based unsupervised FE and TD based unsupervised FE when applied to large p small n problems, it is expected that the KTD based unsupervised FE can be applied in the genomic science domain, which involves many large p small n problems, and in which, the TD based unsupervised FE approach has been effectively applied.
Competing Interest Statement
The authors have declared no competing interest.
