Generalized power method for sparse principal component analysis

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively. While the initial formulations involve nonconvex functions, and are therefore computationally intractable, we rewrite them into the form of an optimization program involving maximization of a convex function on a compact set. The dimension of the search space is decreased enormously if the data matrix has many more columns (variables) than rows. We then propose and analyze a simple gradient method suited for the task. It appears that our algorithm has best convergence properties in the case when either the objective function or the feasible set are strongly convex, which is the case with our single-unit formulations and can be enforced in the block case. Finally, we demonstrate numerically on a set of random and gene expression test problems that our approach outperforms existing algorithms both in quality of the obtained solution and in computational speed. © 2010 Michel Journée, Yurii Nesterov, Peter Richtárik and Rodolphe Sepulchre.

Refining sparse principal components

In this paper, we discuss methods to refine locally optimal solutions of sparse PCA. Starting from a local solution obtained by existing algorithms, these methods take advantage of convex relaxations of the sparse PCA problem to propose a refined solution that is still locally optimal but with a higher objective value. © 2010 Springer -Verlag Berlin Heidelberg.

Broadband terahertz time-domain spectroscopy of drugs-of-abuse and the use of principal component analysis

EPSRC, the European Community IST FP6 Integrated, etc

Studies on principal components and antioxidant activity of different Radix Astragali samples using high-performance liquid chromatography/electrospray ionization multiple-stage tandem mass spectrometry

The principal components, isoflavonoids and astragalosides, in the extract of Radix Astragali were detected by a high-performance liquid chromatography Couple to electrospray ionization ion trap multiple-stage tandem mass spectrometry (HPLC-ESI-IT-MSn) method. By comparing the retention time (t(R)) of HPLC, the ESI-MSn data and the structures of analyzed Compounds with the data of reference compounds and in the literature, 17 isoflavonoids and 12 astragalosides have been identified or tentatively deduced. By Virtue of the extracted ion chromatogram (EIC) mode, simultaneous determination of isoflavonoids and astragalosides could be achieved when the different components formed overlapped peaks. And this method has been utilized to analyze the constituents in extracts of Radix Astragali from Helong City and of different growth years. Then the antioxidant activity of different samples has been Successfully investigated by HPLC-ESI-MS method in multiple selected ion monitoring(MIM) mode, applying the spin trapping technology, and the Ferric Reducing Antioxidant Power (FRAP) assay was applied to support the result.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Numerical simulation of principal tidal constituents in the South China Sea, Gulf of Tonkin and Gulf of Thailand

The principal tidal constituents M-2, S-2, K-1 and O-1 in the South China Sea, Gulf of Tonkin and Gulf of Thailand are simulated simultaneously using the numerical scheme of Kwok et al. (1995 Proceedings of the 1st Asian Computational Fluid Dynamics Conference, pp. 16-19). The average differences between the computed and observed harmonic constants are mostly within 5 cm and 10 degrees for amplitudes and phase-lags, respectively. The simulated tidal regimes in the present model are believed to be more accurate than the previous numerical results. Our studies confirm that a clockwise rotating M-2 amphidromic system lies in the southeast of the Gulf of Thailand and an S-2 amphidromic system at the near-shore area of the northeast South China Sea. The linear tidal energy equation developed by Garrett (1975 Deep-Sea Research 22, 23-35) is generalized to the nonlinear case. Based on the numerical results, the energy budgets in the South China Sea and its subareas, namely the Taiwan Strait, the Gulf of Tonkin, the Gulf of Thailand and the remaining area are investigated. The tidal motion in the Taiwan Strait is maintained mainly by the energy fluxes from the East China Sea for both semidiurnal and diurnal species and partially from the Luzon Strait for semidiurnal species. For the other parts of the South China Sea, the tidal motion is mainly maintained by the energy fluxes through the Luzon Strait. The energy inputs from the tide-generating force are negative for semidiurnal species and positive for diurnal species. (C) 1999 Elsevier Science Ltd. All rights reserved.

ANTRACNOSE: a principal doença da pupunheira: Colletotrichum gloesporioides.

2010