We develop the Double Principal Component Analysis (DPCA) based on a dual factor structure for high-frequency intraday returns contaminated with microstructure noise. The allows noise in addition to efficient log-prices. construct estimators of factors both log-prices and as well their common components, provide uniform consistency these when number assets sampling frequency go infinity. In Mon...

Sparse principal component analysis (PCA) imposes extra constraints or penalty terms to the standard PCA to achieve sparsity. In this paper, we first introduce an efficient algorithm for finding a single sparse principal component (PC) with a specified cardinality. Experiments on synthetic data, randomly generated data and real-world datasets show that our algorithm is very fast, especially on ...

In the presence of outliers, the existing self-organizing rules for Principal Component Analysis (PCA) perform poorly. Using statistical physics techniques including the Gibbs distribution, binary decision fields and effective energies, we propose self-organizing PCA rules which are capable of resisting outliers while fulfilling various PCA-related tasks such as obtaining the first principal co...

A high-performance liquid chromatographic (HPLC) method was established to analyze 36 Chaihu (Radix Bupleuri) samples collected from three species (Bupleurum chinense DC., B. scorzonerifolium Willd. and B. smithii Wolff.). Addition of trifluoroacetic acid into the mobile phase resulted in fingerprint chromatograms with stable baselines. There were thirty-two characteristic peaks in the standard...

Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between d...

Randomly collected samples of parsley and celery from different localities were analyzed to determine their quality based on arsenic concentrations, phenolic constituents the antioxidant capacity edible parts. Arsenic concentrations found in range: root (0.16 μg/g d.m.) < (0.19 leaf (0.35 (0.45 d.m.). Total contents roots similar both species varied significantly depending cultivation site: ...

The main focus in this study is on data pre-processing, reduction in number of inputs or input space size reduction the purpose of which is the justified generalization of data set in smaller dimensions without losing the most significant data. In case the input space is large, the most important input variables can be identified from which insignificant variables are eliminated, or a variable ...