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Prediction by Supervised Principal Components
Gene expression Microarray Regression Survival analysis
2015/8/21
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
The Entire Regularization Path for the Support Vector Machine
Entire Regularization Path Support Vector Machine
2015/8/21
In this paper we argue that the choice of the SVM cost parameter can be critical. We then derive an algorithm that can fit the entire path of SVM solutions for every value of the cost parameter, with ...
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
ON THE “DEGREES OF FREEDOM” OF THE LASSO
Degrees of freedom LARS algorithm lasso model selection SURE unbiased estimate
2015/8/21
We study the effective degrees of freedom of the lasso in the framework of Stein’s unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate for the degr...
The Sentimental Factor:Improving Review Classification via Human-Provided Information
Sentimental Factor Review Classification Human-Provided Information
2015/8/21
Sentiment classification is the task of labeling a review document according to the polarity of its prevailing opinion (favorable or unfavorable). In approaching this problem, a model builder often ha...
Multi-class AdaBoost
boosting exponential loss multi-class classification stagewise modeling
2015/8/21
Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing ...
Multi-class AdaBoost
Boosting Exponential loss Multi-class classification Stagewise modeling
2015/8/21
Boosting has been a very successful technique for solving the two-class classification problem.In going from two-class to multi-class classification, most algorithms have been restricted to reducing t...
NEW MULTICATEGORY BOOSTING ALGORITHMS BASED ON MULTICATEGORY FISHER-CONSISTENT LOSSES
MULTICATEGORY BOOSTING ALGORITHMS MULTICATEGORY FISHER-CONSISTENT LOSSES
2015/8/21
Fisher-consistent loss functions play a fundamental role in the construction of successful binary margin-based classifiers. In this paper we establish the Fisher-consistency condition for multicategor...
We propose a sketch-based sampling algorithm, which effectively exploits the data sparsity. Sampling methods have become popular in large-scale data mining and information retrieval, where high data s...
Margin-constrained Random Projections And Very Sparse Random Projections
Random Projections Sampling Maximum Likelihood Asymptotic Analysis
2015/8/21
We propose methods for improving both the accuracy and efficiency of random projections, the popular dimension reduction technique in machine learning and data mining, particularly useful for estimati...
Margin trees for high-dimensional classification
Maximum margin classifier supprt vector machine decision tree CART
2015/8/21
We propose a method for the classification of more than two classes, from high-dimensional features. Our approach is to build a binary decision tree in a top-down manner, using the optimal margin clas...
L1-regularization path algorithm for generalized linear models
Generalized linear model Lasso Path algorithm Predictor–corrector method Regularization Variable selection
2015/8/21
We introduce a path following algorithm for L1-regularized generalized linear models. The L 1-regularization procedure is useful especially because it, in effect, selects variables according to the am...
Regularized linear discriminant analysis and its application in microarrays
Regularized linear discriminant analysis microarrays
2015/8/21
In this paper, we introduce a modified version of linear discriminant analysis, called the “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of the “near...
Averaged gene expressions for regression
Averaging Hierarchical clustering Lasso Variance reduction
2015/8/21
Although averaging is a simple technique, it plays an important role in reducing variance. We use this essential property of averaging in regression of the DNA microarray data, which poses the challen...
Improving Random Projections Using Marginal Information
Random Projections Marginal Information
2015/8/21
We present an improved version of random projections that takes advantage of marginal norms. Using a maximum likelihood estimator (MLE), marginconstrained random projections can improve estimation acc...