query: Kernel PCA for feature selection for various machine learning algorithms
positive_passages.docid: 143724
positive_passages.text: Is it possible to use kernel PCA for feature selection?
negative_passages.docid: 29006
negative_passages.text: Kernel ridge regression with matrix-vector data set $S := \{ X_i, y_i \}_{i=1}^{N}$?
negative_passages.docid: 155433
negative_passages.text: Examples of PCA where PCs with low variance are "useful"
negative_passages.docid: 21224
negative_passages.text: Applying PCA to data that fail a normality test
negative_passages.docid: 118753
negative_passages.text: Is feature selection with dummy coding of categorical variables problematic?
negative_passages.docid: 103877
negative_passages.text: The advantages and disadvantages of using Kaiser Rule to select the number of principal components
negative_passages.docid: 102449
negative_passages.text: Weighting features prior to SVM
negative_passages.docid: 90679
negative_passages.text: Algebra of LDA. Fisher discrimination power of a variable and Linear Discriminant Analysis
negative_passages.docid: 76619
negative_passages.text: Computing kernels of a matrix in distinct cases
negative_passages.docid: 79976
negative_passages.text: Feature Scaling and Mean Normalization
negative_passages.docid: 21651
negative_passages.text: Estimating the most important features in a k-means cluster partition
negative_passages.docid: 154304
negative_passages.text: Do PCA affect different classification methods?
negative_passages.docid: 115584
negative_passages.text: Dataset transformation for clustering after PCA
negative_passages.docid: 27143
negative_passages.text: Detecting significant predictors out of many independent variables
negative_passages.docid: 2632
negative_passages.text: PCA and the train/test split
negative_passages.docid: 112134
negative_passages.text: What are principal component scores?
negative_passages.docid: 108282
negative_passages.text: How to choose between learning algorithms