query: linear versus nonlinear dimensionality reduction techniques
positive_passages.docid: 34931
positive_passages.text: How to understand "nonlinear" as in "nonlinear dimensionality reduction"?
negative_passages.docid: 145064
negative_passages.text: Techniques to detect overfitting
negative_passages.docid: 131784
negative_passages.text: FindFaces: how to improve the results of the face recognition feature
negative_passages.docid: 112991
negative_passages.text: What algorithms need feature scaling, beside from SVM?
negative_passages.docid: 25481
negative_passages.text: Minimum set of features that can distinguish all objects
negative_passages.docid: 46076
negative_passages.text: Least Squares with $ {L}_{2} $ (L2) Linear Norm Regularization (Not Squared)
negative_passages.docid: 15196
negative_passages.text: Why scaling is important for the linear SVM classification?
negative_passages.docid: 57060
negative_passages.text: Is normalizing the features always good for classification?
negative_passages.docid: 98369
negative_passages.text: Reducing number of point features considering minimal distance and spatial distribution
negative_passages.docid: 90679
negative_passages.text: Algebra of LDA. Fisher discrimination power of a variable and Linear Discriminant Analysis
negative_passages.docid: 147748
negative_passages.text: Issues with feature selection in matlab
negative_passages.docid: 125435
negative_passages.text: Practical application for linear transformations
negative_passages.docid: 63692
negative_passages.text: Is KNN a discriminative learning algorithm?
negative_passages.docid: 30889
negative_passages.text: Classifying complex $2\times 2$ matrices up to similarity
negative_passages.docid: 135217
negative_passages.text: Least squares fit of a bivariate quadratic-linear product to an oriented point set
negative_passages.docid: 14183
negative_passages.text: Why Lasso or ElasticNet perform better than Ridge when the features are correlated
negative_passages.docid: 140116
negative_passages.text: NonLinearFit with data