The detection of blood-borne prostate cancer (PCA) cells may help with clinical staging and the further understanding of PCA metastases. We discovered prostate-specific antigen (PSA)-positive stained but not PSA mRNA-expressing blood cells by means of cell sorting and PSA reverse transcription-PCR in patients. Therefore, we developed a cytokeratin immunomagnetic method to isolate PSA-positive e...

this paper presents an integrated data envelopment analysis (dea) – principal component analysis (pca) – analytical hierarchy process (ahp) to achieve the efficiency scores and ranks of the insurance companies. fourteen insurance companies with thirteen input and output variables have been considered for the purpose of this study. since the dea model is sensitive to the number of variables in c...

METHOD Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the mole...

Among various image fusion methods, principal component analysis (PCA) technique is capable of quickly merging the massive volumes of data. For IKONOS imagery, PCA can yield satisfactory “spatial” enhancement but may introduce spectral distortion, appearing as a change in colors between compositions of resembled and fused multi-spectral bands. To solve this problem, a fast improved PCA fusion m...

In this project, we studied PCA based appearance model in visual tracking. We implemented PCA AM module in MTF framework and attempted different ways of formulating PCA. In the experiment sections, we demonstrate the tracking performance of offline PCA when combined with different search methods. PCA + Forward Compositional has shown great tracking accuracy and robustness. It indicates a good p...

A unified framework based on the dynamic principal component analysis (PCA) is proposed for performance monitoring of constrained multi-variable model predictive control (MPC) systems. In the proposed performance monitoring framework, the dynamic PCA based performance benchmark is adopted for performance assessment, while performance diagnosis is carried out using a unified weighted dynamic PCA...

We propose a simple yet efficient feature-selection method — based on principle component analysis (PCA) — for SVM-based classifiers. The idea is to select features whose corresponding axes are closest to the principle components computed from a data distribution by PCA. Experimental results show that our proposed method reduces dimensionality similar to PCA, but maintains the original measurem...

classical lbp such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. in this paper, we introduce an improved lbp algorithm to solve these problems that utilizes fast pca algorithm for reduction of vector dimensions of extracted features. in other words, proffer method (fast pca+lbp) is an improved lbp algorithm that is extracted ...

In this article we propose a novel Wavelet Packet Decomposition (WPD)-based modification of the classical Principal Component Analysis (PCA)-based face recognition method. The proposed modification allows to use PCA-based face recognition with a large number of training images and perform training much faster than using the traditional PCA-based method. The proposed method was tested with a dat...