We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersortstanfordedu/)

/pdf/robust-enumeration-of-cell-subsets-from-tissue-expression-2prfdfkgtp.pdf

Robust enumeration of cell subsets from tissue expression profiles

On robust estimation of the location parameter

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

http://svms.org/parameters/CherkasskyMa2004.pdf

Practical selection of SVM parameters and noise estimation for SVM regression

Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields - time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.

Support Vector Regression

We discuss empirical comparison of analytical methods for model selection. Currently, there is no consensus on the best method for finite-sample estimation problems, even for the simple case of linear estimators. This article presents empirical comparisons between classical statistical methods--Akaike information criterion (AIC) and Bayesian information criterion (BIC)--and the structural risk minimization (SRM) method, based on Vapnik-Chervonenkis (VC) theory, for regression problems. Our study is motivated by empirical comparisons in Hastie, Tibshirani, and Friedman (2001), which claims that the SRM method performs poorly for model selection and suggests that AIC yields superior predictive performance. Hence, we present empirical comparisons for various data sets and different types of estimators (linear, subset selection, and k-nearest neighbor regression). Our results demonstrate the practical advantages of VC-based model selection; it consistently outperforms AIC for all data sets. In our study, SRM and BIC methods show similar predictive performance. This discrepancy (between empirical results obtained using the same data) is caused by methodological drawbacks in Hastie et al. (2001), especially in their loose interpretation and application of SRM method. Hence, we discuss methodological issues important for meaningful comparisons and practical application of SRM method. We also point out the importance of accurate estimation of model complexity (VC-dimension) for empirical comparisons and propose a new practical estimate of model complexity for k-nearest neighbors regression.

/pdf/comparison-of-model-selection-for-regression-1oghpx74sq.pdf

Comparison of model selection for regression

We propose practical recommendations for selecting metaparameters for SVM regression (that is, ? -insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. Good generalization performance of the proposed parameter selection is demonstrated empirically using several lowdimensional and high-dimensional regression problems. In addition, we compare generalization performance of SVM regression (with proposed choice?) with robust regression using 'least-modulus' loss function (? =0). These comparisons indicate superior generalization performance of SVM regression.

http://www.svms.org/parameters/CherkasskyMa2002.pdf

Selection of Meta-parameters for Support Vector Regression

This paper presents a new learning formulation for multiple model estimation (MME). Under this formulation, training data samples are generated by several (unknown) statistical models. Hence, most existing learning methods (for classification or regression) based on a single model formulation are no longer applicable. We describe a general framework for MME. Then we introduce a constructive support vector machine (SVM)-based methodology for multiple regression estimation. Several empirical comparisons using synthetic and real-life data sets are presented to illustrate the proposed approach for multiple model regression formulation.

Multiple model regression estimation

This paper addresses selection of the loss function for regression problems with finite data. It is well-known (under standard regression formulation) that for a known noise density there exist an optimal loss function under an asymptotic setting (large number of samples), i.e. squared loss is optimal for Gaussian noise density. However, in real-life applications the noise density is unknown and the number of training samples is finite. For such practical situations, we suggest using Vapnik's /spl epsiv/-insensitive loss function. We use practical method for setting the value of /spl epsiv/ as a function of known number of samples and (known or estimated) noise variance (V. Cherkassky and Y. Ma, (2004), (2002)). We consider commonly used noise densities (such as Gaussian, Uniform and Laplacian noise). Empirical comparisons for several representative linear regression problems indicate that Vapnik's /spl epsiv/-insensitive loss yields more robust performance and improved prediction accuracy, in comparison with squared loss and least-modulus loss, especially for noisy high-dimensional data sets.

Yunqian Ma

Papers

Practical selection of SVM parameters and noise estimation for SVM regression

Comparison of model selection for regression

Selection of Meta-parameters for Support Vector Regression

Multiple model regression estimation

Comparison of loss functions for linear regression