In this paper, we revisit the problem of clustering 1318 new variable stars found in Milky way. Our recent work distinguishes these based on their light curves which are univariate series brightness from observed at discrete time points. This proposes a approach to look as continuous over by transforming them into functional data. Then, principal component analysis is performed using curves. Cl...

This paper proposes the use of neutral reference models to detect local emotional prominence in the fundamental frequency. A novel approach based on functional data analysis (FDA) is presented, which aims to capture the intrinsic variability of F0 contours. The neutral models are represented by a basis of functions and the testing F0 contour is characterized by the projections onto that basis. ...

when the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. in this paper, we propose a supervised feature extraction method based on discriminant analysis (da) which uses the first principal component (pc1) to weight the scatter matrices. the proposed method, called da-pc1, copes with the small sample size problem and has...

t is the purpose of this paper to contribute to the discussion initiated bywachter about the parallelism between principal component (pc) and atypological grade of membership (gom) analysis. the author testedempirically the close relationship between both analysis in a lowdimensional framework comprising up to nine dichotomous variables and twotypologies. our contribution to the subject is also...

Functional Principal Component Analysis (FPCA) provides a powerful and natural way to model functional financial data sets (such as collections of time-indexed futures and interest rate yield curves). However, FPCA assumes each sample curve is drawn from an independent and identical distribution. This assumption is axiomatically inconsistent with financial data; rather, samples are often interl...