Principal components analysis (PCA) is the optimal linear encoder of data. Sparse linear encoders (e.g., sparse PCA) produce more interpretable features that can promote better generalization. (i) Given a level of sparsity, what is the best approximation to PCA? (ii) Are there efficient algorithms which can achieve this optimal combinatorial tradeoff? We answer both questions by providing the f...

which is solved in implicit plasma simulations. Compared with the standard Helmholtz equation [4, p68], (c∆t/2) replaces −(c/ω), and Eq. 1 is solved each time step (instead of for each value of ω) for E with S given. The source term, S, is neither divergence nor curl-free in general. Specifically, we ask under what conditions charge conservation is satisfied. Namely, do numerical solutions of E...

In this paper we propose a new approach for learning a summarized representation of high dimensional continuous data. We apply the model to learn efficient representations of grasp data for two robotic scenarios that facilitates a compact summarization. Our technique consists of a Bayesian non-parametric model capable of encoding highdimensional data from complex distributions using a sparse su...

BACKGROUND The present study aimed to compare the expression of liver kinase B1 (LKB1) in prostate cancer (PCa) tissues and the paired adjacent tissues, then to evaluate the statistical relationship between LKB1 expression and prognosis of PCa patients. MATERIAL AND METHODS The relative expression of LKB1 at mRNA level was detected by quantitative real-time polymerase chain reaction (qRT-PCR). ...

Table 1. June water chemistry values for Nine Pipe and Ovando Valley wetland study sites. Data from Borth (1998). Table 2. July water chemistry values for Nine Pipe and Ovando Valley wetland study sites. Data from Borth (1998). Table 3. August water chemistry values for Nine Pipe and Ovando Valley wetland study sites. Data from Borth (1998). Table 4. June, July and August average water chemistr...

The aim of the present study was to investigate the prognostic role of metallothionein-2A (MT-2A), E-cadherin, interleukin-6 (IL-6), cyclin-E, proliferating cell nuclear antigen (PCNA) and B cell lymphoma (Bcl)-2 in the biochemical recurrence of prostate cancer (PCa) using tissue microarray immunostaining. Tissue specimens from 128 PCa patients who underwent radical prostatectomy were processed...

We study the problem of estimating the leading eigenvectors of a highdimensional population covariance matrix based on independent Gaussian observations. We establish a lower bound on the minimax risk of estimators under the l2 loss, in the joint limit as dimension and sample size increase to infinity, under various models of sparsity for the population eigenvectors. The lower bound on the risk...

The recent paper (Cheung 2001) has studied the blind identification of Gaussian source process through a general temporal independent component analysis (ICA) approach named dual autoregressive modelling. It is actually a temporal extension of the classical principal component analysis without considering the principal order of the components. In this paper, we will further show the identifiabl...

Facial Expression Recognition is one of the active research area in the field of Human Machine Interaction (HMI) because of its several applications such as human emotion analysis, stress level and lie detection. In this paper, an algorithm for facial expression recognition has been proposed which integrate the Local Binary Patterns (LBP), Gabor filter and Principal Component Analysis (PCA). Th...

The conventional principal component analysis (PCA) and Fisher linear discriminant analysis (FLD) are both based on vectors. Rather, in this paper, a novel PCA technique directly based on original image matrices is developed for image feature extraction. Experimental results on ORL face database show that the proposed IMPCA are more powerful and e:cient than conventional PCA and FLD. ? 2002 Pat...