Feature Generation Method by Geometrical Interpretation of Fisher Linear Discriminant Analysis
نویسندگان
چکیده
منابع مشابه
Fisher Discriminant Analysis (FDA), a supervised feature reduction method in seismic object detection
Automatic processes on seismic data using pattern recognition is one of the interesting fields in geophysical data interpretation. One part is the seismic object detection using different supervised classification methods that finally has an output as a probability cube. Object detection process starts with generating a pickset of two classes labeled as object and non-object and then selecting ...
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 کاملSparsifying the Fisher Linear Discriminant by Rotation.
Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis (LDA). To efficiently use them, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the needed sparsity. In this paper, we propose a family of rotations to c...
متن کاملFace Recognition by Fisher and Scatter Linear Discriminant Analysis
Fisher linear discriminant analysis (FLDA) based on variance ratio is compared with scatter linear discriminant (SLDA) analysis based on determinant ratio. It is shown that each optimal FLDA data model is optimal SLDA data model but not opposite. The novel algorithm 2SS4LDA (two singular subspaces for LDA) is presented using two singular value decompositions applied directly to normalized multi...
متن کاملPrincipal Component Analysis and Fisher Linear Discriminant Analysis
Principal Components Analysis (PCA) is an appearance based technique used widely for the dimensionality reduction and it records a great performance in face recognition. PCA based approaches typically include two phases: training and classification (Draper et al 2003). In the training phase, an Eigen space is established from the training samples using PCA and the training face images are mappe...
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: IEEJ Transactions on Electronics, Information and Systems
سال: 2007
ISSN: 0385-4221,1348-8155
DOI: 10.1541/ieejeiss.127.831