From the time the first computers built, experts always tried to find out how to improve the performance of computers in processing a large volume of information in a way that acquire more accuracy by spending less time and resources. By the daily growth of data and the noise inside them, experts concluded that data have to be processed, too, before entering the algorithm as input. This is called pre-processing. Nowadays, dimensionality reduction is one of the most important pre-processing techniques used in different sciences. Feature extraction is a dimensionality reduction technique and it is a collection of methods which try to reduce the dimension of data by decreasing the number of effective features in the data. Feature extraction methods are broadly divided into two groups: stochastic features extraction methods and probabilistic feature extraction methods. In stochastic methods, only linear or non-linear transformations are used to derive a new feature space where the dimensionality of data reduces when they mapped to this new space. On the other hand, Probabilistic methods try to derive a new feature space by adding noise to the model and considering a probabilistic distribution for each model parameters. Canonical Correlation Analysis (CCA) is a well-known stochastic feature extraction method. In this study we assessed different aspects of feature extraction; mainly the CCA method and we proposed a new probabilistic model for CCA along with a mixture of probabilistic CCA model. The proposed methods are evaluated in a face recognition application and the results showed that using these techniques, the justify; MARGIN: 30pt 0in; unicode-bidi: embed; DIRECTION: ltr" Keywords : dimensionality reduction, feature extraction, canonical correlation analysis
