Statistical modeling technique has pivotal role in better understanding of the software development processes. Among them neural network techniques have enhanced predictive capability than most other statistical models. This paper explains the application of principal component analysis to neural network modeling as a way to improve predictability of neural network. The purpose of principal com...

We introduce an efficient statistical modeling technique called Mixture of Principal Components (MPC). This model is a linear extension to the traditional Principal Component Analysis (PCA) and uses a mixture of eigenspaces to capture data variations. We use the model to capture face appearance variations due to pose and lighting changes. We show that this more efficient modeling leads to impro...

We propose a novel network traffic matrix decomposition method named Stable Principal Component Pursuit with FrequencyDomain Regularization (SPCP-FDR), which improves the Stable Principal Component Pursuit (SPCP) method by using a frequency-domain noise regularization function. An experiment demonstrates the feasibility of this new decomposition method. key words: Traffic Matrix, Stable Princip...

PCA has find out its most important application in the field of linear algebra. PCA is a method of extracting information from confusing data sets so used in various fields like neuroscience, computer graphics, etc [19]. PCA is a vector space transform often used to reduce multidimensional data sets to lower dimensions for analysis. Depending on the field of application, it is also named the di...

Probabilistic Principal Component Analysis is a reformulation of the common multivariate analysis technique known as Principal Component Analysis. It employs a latent variable model framework similar to factor analysis allowing to establish a maximum likelihood solution for the parameters that comprise the model. One of the main assumptions of Probabilistic Principal Component Analysis is that ...

drastic is known as the most prototype models of groundwater vulnerability assessment. the drastic constitutes from seven schematic parameters consisting: depth to groundwater, recharge to aquifer, aquifer geology, and surface soil texture, impact of vadoze zone and hydraulic conductivity. in this study the models parameters were extracted by the main schematic maps of model. instead using the ...

The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combin...

The Sivas Basin is one of the most well known major Tertiary basins of the Anatolia connected with the evolution of the Neotethyan Ocean. Central Anatolian Thrust Belt contains realms of this ocean and bounds the basin from north. Therefore, realms of the Inner Tauride Ocean are also surrounded the basin from southeast and represented with allochthonous units. Axis of the eastern section of thi...