A particularly challenging context for dimensionality reduction is multivariate circular data, that is, data supported on a torus. Such kind of appears, example, in the analysis various phenomena environmental sciences and astronomy, as well molecular structures. This article introduces Scaled Torus Principal Component Analysis (ST-PCA), novel approach to perform with toroidal data. ST-PCA find...

Principal component analysis has been widely adopted to reduce the dimension of data while preserving information. The quantum version PCA (qPCA) can be used analyze an unknown low-rank density matrix by rapidly revealing principal components it, i.e. eigenvectors with largest eigenvalues. However, due substantial resource requirement, its experimental implementation remains challenging. Here, ...

of Japanese Kanji using principal component analysis as a preprocessor to an articial neural etwork.

Abstract. This paper reports on elemental factor analyses of the innovativeness study in the Turkish manufacturing industry, drawing on a sample of 184 manufacturing firms. Factor structures are constructed in order to empirically test a framework identifying the relationships among innovativeness, performance and determinants of innovation. After several independent principal component analyse...