scholarly journals Sparse Covariance Estimates for High Dimensional Classification Using the Cholesky Decomposition

2006 ◽  
pp. 835-843
Author(s):  
Asbjørn Berge ◽  
Anne Schistad Solberg
Keyword(s):  
Cholesky Decomposition ◽  
High Dimensional ◽  
Dimensional Classification

scholarly journals Bias-Corrected Diagonal Discriminant Rules for High-Dimensional Classification

Biometrics ◽  
2010 ◽  
Vol 66 (4) ◽  
pp. 1096-1106 ◽  
Author(s):  
Song Huang ◽  
Tiejun Tong ◽  
Hongyu Zhao
Keyword(s):  
High Dimensional ◽  
Dimensional Classification

scholarly journals A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

2014 ◽  
Vol 5 (3) ◽  
pp. 82-96 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Sanja Pfeifer ◽  
Nataša Šarlija
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Classification Problem ◽  
High Dimensional ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dimensional Classification ◽  
Artificial Neural

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

Sparse additive machine with ramp loss

2020 ◽  
pp. 1-20
Author(s):  
Hong Chen ◽  
Changying Guo ◽  
Huijuan Xiong ◽  
Yingjie Wang
Keyword(s):  
Descent Method ◽  
Misclassification Error ◽  
High Dimensional ◽  
Block Coordinate Descent ◽  
Regularization Scheme ◽  
Hinge Loss ◽  
Dimensional Classification ◽  
Benchmark Datasets ◽  
Ramp Loss ◽  
Error Decomposition

Sparse additive machines (SAMs) have attracted increasing attention in high dimensional classification due to their representation flexibility and interpretability. However, most of existing methods are formulated under Tikhonov regularization scheme with the hinge loss, which are susceptible to outliers. To circumvent this problem, we propose a sparse additive machine with ramp loss (called ramp-SAM) to tackle classification and variable selection simultaneously. Misclassification error bound is established for ramp-SAM with the help of detailed error decomposition and constructive hypothesis error analysis. To solve the nonsmooth and nonconvex ramp-SAM, a proximal block coordinate descent method is presented with convergence guarantees. The empirical effectiveness of our model is confirmed on simulated and benchmark datasets.

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