A Trivial Linear Discriminant Function
نویسندگان
چکیده
منابع مشابه
A Trivial Linear Discriminant Function
In this paper, we focus on the new model selection procedure of the discriminant analysis. Combining resampling technique with k-fold cross validation, we develop a k-fold cross validation for small sample method. By this breakthrough, we obtain the mean error rate in the validation samples (M2) and the 95% confidence interval (CI) of discriminant coefficient. Moreover, we propose the model sel...
متن کاملA Multi Linear Discriminant Analysis Method Using a Subtraction Criteria
Linear dimension reduction has been used in different application such as image processing and pattern recognition. All these data folds the original data to vectors and project them to an small dimensions. But in some applications such we may face with data that are not vectors such as image data. Folding the multidimensional data to vectors causes curse of dimensionality and mixed the differe...
متن کاملWeb Page Quality Estimation Based on Linear Discriminant Function
With the growth of web data, how to estimate web page quality effectively and rapidly becomes more and more important for web information retrieval and knowledge discovery. This paper analyzes the differences between retrieval target pages and ordinary pages using query-independent features. Using these features, an algorithm called Linear Page Estimation (LPE) is proposed for web page quality ...
متن کاملFisher Linear Discriminant Analysis
Fisher Linear Discriminant Analysis (also called Linear Discriminant Analysis(LDA)) are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later c...
متن کاملSeparable linear discriminant analysis
Linear discriminant analysis (LDA) is a popular technique for supervised dimension reduction. Due to the curse of dimensionality usually suffered by LDA when applied to 2D data, several two-dimensional LDA (2DLDA) methods have been proposed in recent years. Among which, the Y2DLDA method, introduced by Ye et al. (2005), is an important development. The idea is to utilize the underlying 2D data ...
متن کاملذخیره 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: Statistics, Optimization & Information Computing
سال: 2015
ISSN: 2310-5070,2311-004X
DOI: 10.19139/soic.v3i4.151