Low bit rate parametric coding of multichannel audio is mainly based on Binaural Cue Coding (BCC). Another multichannel audio processing method called upmix can also be used to deliver multichannel audio, typically 5.1 signals, at low data rates. More precisely, we focus on existing upmix method based on Principal Component Analysis (PCA). This PCA-based upmix method aims at blindly create a re...

The selection of appropriate wavelets is an important target for any application. In this paper Face recognition has been performed using Principal component analysis (PCA), Gaussian based PCA and Gabor based PCA. PCA extracts the relevant information from complex data sets and provides a solution to reduce dimensionality. PCA is based on Euclidean distance calculation which is minimized by app...

BACKGROUND Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use ...

We propose a novel convex relaxation of sparse principal subspace estimation based on the convex hull of rank-d projection matrices (the Fantope). The convex problem can be solved efficiently using alternating direction method of multipliers (ADMM). We establish a near-optimal convergence rate, in terms of the sparsity, ambient dimension, and sample size, for estimation of the principal subspac...

Principal components analysis (PCA) is a popular descriptive multivariate method for handling quantitative data. In PCA of a mixture of quantitative and qualitative data, it requires quantification of qualitative data to obtain optimal scaling data and use ordinary PCA. The extended PCA including such quantification is called nonlinear PCA, see Gifi [Gifi, 1990]. The existing algorithms for non...

This paper discusses a new highly robust learning algorithm for exploring local principal component analysis (PCA) structures in which an observed data follow one of several heterogeneous PCA models. The proposed method is formulated by minimizing β-divergence. It searches a local PCA structure based on an initial location of the shifting parameter and a value of the tuning parameter β. If the ...

linear–dendrite copolymers containing hyper branched poly(citric acid) and linear poly(ethylene glycol) blocks pca–peg–pca are promising nonmaterial to use  in nanomedicine. to investigate their potential application in biological systems (especially for drug carries) oniom2 calculations were applied to study the nature of particular interactions between drug and the polymeric nanoparticles.  b...

A face recognition algorithm based on modular PCA approach is presented in this paper. The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in lighting direction and facial expression. In the proposed technique, the face images are divided into smaller sub-images and the PCA approach is applied to each of the...

Statistical models have opened up new possibilities for the automated analysis of images. However, the limited availability of representative training data, e.g. segmented images, leads to a bottleneck for the application of statistical models in practice. In this paper, we propose a novel patch-based technique that enables to learn representative statistical models of shape, appearance, or mot...

Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we show that PCA and PCO can be carried out under regression frameworks. Thus, it is convenient to incorporate sparse techniques into the regression frameworks. In particular, we propose a sparse PCA model and a sparse PCO model. The for...