MLAI/DimensionalityReduction
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Canonical Correlation AnalysisMLAI/DimensionalityReduction 2020. 1. 25. 17:57
1. Overview Canonical Correlation Analysis (CCA) as a good prediction model. Because CCA well explains data dependency between input and output. So CCA can minimize the prediction error. CCA finds pairs of basis that maximize the correlation between two variables x and y in subspace. When we perform the regression in the reduced space, the fitting errors are minimized because two variables are h..
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Difference PCA and Factor analysisMLAI/DimensionalityReduction 2020. 1. 23. 11:06
1. Overview Both are dimension reduction techniques, but while Principal Component Analysis is used to reduce the number of variables by creating principal components, extracting the essence of the dataset in the means of artificially created variables, which best describe the variance of the data. Factor Analysis tries to identify, unknown latent variables to explain the original data. Often pr..
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Feature selectionMLAI/DimensionalityReduction 2019. 10. 6. 14:59
1. Overview In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/us..
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Fisher's linear discriminant(Linear discriminant analysis, LDA)MLAI/DimensionalityReduction 2019. 10. 5. 22:25
1. Overview Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistic, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a..
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Principal component analysis(PCA)MLAI/DimensionalityReduction 2019. 10. 5. 17:32
1. Overview Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal compo..