Exponential Possibility Discriminant Analysis

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Incremental and Decremental Exponential Discriminant Analysis for Face Recognition

Linear Discriminant Analysis (LDA) is widely used for feature extraction in face recognition but suffers from small sample size (SSS) problem in its original formulation. Exponential discriminant analysis (EDA) is one of the variants of LDA suggested recently to overcome this problem. For many real time systems, it may not be feasible to have all the data samples in advance before the actual mo...

متن کامل

Portfolio selection based on upper and lower exponential possibility distributions

In this paper, two kinds of possibility distributions, namely, upper and lower possibility distributions are identi®ed to re ̄ect experts' knowledge in portfolio selection problems. Portfolio selection models based on these two kinds of distributions are formulated by quadratic programming problems. It can be said that a portfolio return based on the lower possibility distribution has smaller po...

متن کامل

Combination of Local Multiple Patterns and Exponential Discriminant Analysis for Facial Recognition

Global features-based methods and local features –based methods have been very successful in face recognition system, yet they can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier. We consider both descriptor for face images with Local Multiple Pattern, and discriminant learning techniques with Exponential Discriminant Analysis. A combinati...

متن کامل

Performance Evaluation of Exponential Discriminant Analysis with Feature Selection for Steganalysis

The performance of supervised learning-based seganalysis depends on the choice of both classifier and features which represent the image. Features extracted from images may contain irrelevant and redundant features which makes them inefficient for machine learning. Relevant features not only decrease the processing time to train a classifier but also provide better generalisation. Linear discri...

متن کامل

Clustering Using Local and Global Exponential Discriminant Regularization

In recently reported clustering approaches, both local and global information were utilized in order to effectively learn nonlinear manifold in image dataset. However, in each of these clustering approaches, regularization parameter had to be included to handle small-sample-size (SSS) problem of linear discriminant analysis (LDA). Due to which, we have to optimize a number of clustering paramet...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Japan Society for Fuzzy Theory and Systems

سال: 1994

ISSN: 0915-647X,2432-9932

DOI: 10.3156/jfuzzy.6.6_1147