پديد آورنده :
افرابندپي، همايون
عنوان :
ارائه يك مدل استخراج ويژگي مخلوط احتمالاتي مبتني بر روش تحليل همبستگي هاي پايه دو بعدي
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
هوش مصنوعي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر
صفحه شمار :
هشت، 79ص.: مصور، جدول، نمودار
يادداشت :
ص.ع. به فارسي و انگليسي
استاد راهنما :
مهران صفاياني
استاد مشاور :
عبدالرضا ميرزايي
توصيفگر ها :
كاهش ابعاد داده ها , استخراج ويژگي مبتني بر احتمال
تاريخ نمايه سازي :
3/2/94
استاد داور :
محمدرضا احمدزاده
تاريخ ورود اطلاعات :
1396/10/02
رشته تحصيلي :
برق و كامپيوتر
دانشكده :
مهندسي برق و كامپيوتر
چكيده انگليسي :
Mixture of Probabilistic Two Dimensional Canonical Correlation Analysis Homayun Afrabandpey h afraei@ec iut ac ir 2014 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Suppervisor Dr Mehran Safayani safayani@cc iut ac ir Abstract 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 classification accuracy improved well Keywords dimensionality reduction feature extraction canonical correlation analysisPDF created with pdfFactory Pro trial version www pdffactory com
استاد راهنما :
مهران صفاياني
استاد مشاور :
عبدالرضا ميرزايي
استاد داور :
محمدرضا احمدزاده
