Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional PCA and LDA some weaknesses. In this paper, we propose a new Line-based methodes called Line-based PCA and Line-based LDA that ...

AIMS Genes expressed only in cancer tissue or specific organs will be useful molecular markers. To identify genes that encode secreted proteins present in prostate cancer (PCa), we generated Escherichia coli ampicillin secretion trap (CAST) libraries from PCa and normal prostate (NP). METHODS AND RESULTS We identified 15 candidate genes that encode secreted proteins present in PCa and NP. Qua...

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 nanoparticle...

Principal component analysis (PCA) is a ubiquitous statistical technique for data analysis. PCA is however limited by its linearity and may sometimes be too simple for dealing with real-world data especially when the relations among variables are nonlinear. Recent years have witnessed the emergence of nonlinear generalizations of PCA, as for instance nonlinear principal component analysis (NLPC...

Exclusion and not using of rangeland in the long term affects the composition and homogeneity of vegetation and consequently leads to the improvement of plants status. In this study, the characteristics and structural changes of the rangeland of Gonbad, Hamadan province, Iran, in 2014 (after 20 years of enclosure) were evaluated using Braun-Blanquet plot, Phytosociology and multivariate analysi...

OBJECTIVE Secreted apoptosis-related protein (SARP) families are considered to counteract the oncogenic Wnt signaling pathway, and inactivation of this gene may aid cancer development and progression. Recently, the aberrant methylation of SARP2 was detected frequently in pancreatic carcinoma (PCa) tissue, but not in normal pancreatic tissue. We evaluated the hypermethylation of SARP2 in pure pa...

Individual observations can be very influential when performing classical Principal Component Analysis in a Euclidean space. Robust PCA algorithms detect and neutralize such dominating data points. This paper studies robustness issues for PCA in a kernel induced feature space. The sensitivity of Kernel PCA is characterized by calculating the influence function. A robust Kernel PCA method is pro...

By analyzing the direction characteristic of principal component analysis (PCA), we propose an edge detection method based on PCA. Using Karhunen–Loëve transform, PCA transforms the original dataset into lower-dimensional feature data. The transform has directivity both on energy accumulation and data selection. The author points out and proves the two direction characteristics. In this paper, ...

We consider the dimensionality-reduction problem for a contaminated data set in a very high dimensional space, i.e., the problem of finding a subspace approximation of observed data, where the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some outlying observations. We propose a High-dimension Robust Principal Component...

Face recognition systems have enhanced human-computer interactions in the last ten years. However, literature reveals that current techniques used for identifying or verifying faces are not immune to limitations. Principal Component Analysis-Support Vector Machine (PCA-SVM) and Analysis-Artificial Neural Network (PCA-ANN) among relatively recent powerful face analysis techniques. Compared PCA-A...