Feature-Inclusion Stochastic Search for Gaussian Graphical Models

We describe a serial algorithm called feature-inclusion stochastic search, or FINCS, that uses online estimates of edge-inclusion probabilities to guide Bayesian model determination in Gaussian graphical models. FINCS is compared to MCMC, to Metropolis-based search methods, and to the popular lasso; it is found to be superior along a variety of dimensions, leading to better sets of discovered models, greater speed and stability, and reasonable estimates of edge-inclusion probabilities. We illustrate FINCS on an example involving mutual-fund data, where we compare the model-averaged predictive performance of models discovered with FINCS to those discovered by competing methods.     

Computer aided measurement of melanoma depth of invasion in microscopic images

This paper presents a novel computer aided technique for measurement of melanoma depth of invasion. Melanoma is the deadliest form of skin cancer with worldwide increasing incidences. For a conclusive diagnosis of melanoma, skin biopsies should be examined under a microscope. Visual inspection of microscopic samples is often subjective, time-consuming, cumbersome and prone to human errors. This fact demonstrates the necessity of developing an automated method which assists pathologists in evaluating histopathological samples more accurately in the busy clinical environment. To the best of our knowledge, this is the first time that a computer-assisted diagnosis algorithm has been applied in measurement of melanoma invasion depth. The proposed method uses a clustering algorithm for granular layer extraction and a pre-trained SVM classifier for detection of malignant melanocytes. The experimental results with average error of 3.9 μm demonstrate that the proposed method is reliable and effective.     

Prediction analysis and quality assessment of microwell array images

Microwell arrays are widely used for the analysis of fluorescent‐labelled biomaterials. For rapid detection and automated analysis of microwell arrays, the computational image analysis is required. Support Vector Machines (SVM) can be used for this task. Here, we present a SVM‐based approach for the analysis of microwell arrays consisting of three distinct steps: labeling, training for feature selection, and classification into three classes. The three classes are filled, partially filled, and unfilled microwells. Next, the partially filled wells are analyzed by SVM and their tendency towards filled or unfilled tested through applying a Gaussian filter. Through this, all microwells can be categorized as either filled or unfilled by our algorithm. Therefore, this SVM‐based computational image analysis allows for an accurate and simple classification of microwell arrays.     

主成分分析-支持向量机用于肝病分类模型

将主成分分析(PCA)用于肝功能检测数据特征提取,然后用支持向量机(SVM)对乙肝、丙肝、肝硬化、正常人样本建立分类模型。采用高斯径向基函数(RBF)为核函数,调节核函数参数C及σ以建立最佳支持向量机模型。该模型对训练集的识别率为99.3%,对预测集的预测率为96.4%。结果表明:PCA-SVM法建立的肝病分类模型能较好的区分乙肝、丙肝、肝硬化及正常人,且分类效果优于传统支持向量机及人工神经网络(ANN)分类模型。     

Estimation Stability With Cross-Validation (ESCV)

Cross-validation (CV) is often used to select the regularization parameter in high-dimensional problems. However, when applied to the sparse modeling method Lasso, CV leads to models that are unstable in high-dimensions, and consequently not suited for reliable interpretation. In this article, we propose a model-free criterion ESCV based on a new estimation stability (ES) metric and CV. Our proposed ESCV finds a smaller and locally ES-optimal model smaller than the CV choice so that it fits the data and also enjoys estimation stability property. We demonstrate that ESCV is an effective alternative to CV at a similar easily parallelizable computational cost. In particular, we compare the two approaches with respect to several performance measures when applied to the Lasso on both simulated and real datasets. For dependent predictors common in practice, our main finding is that ESCV cuts down false positive rates often by a large margin, while sacrificing little of true positive rates. ESCV usually outperforms CV in terms of parameter estimation while giving similar performance as CV in terms of prediction. For the two real datasets from neuroscience and cell biology, the models found by ESCV are less than half of the model sizes by CV, but preserves CV's predictive performance and corroborates with subject knowledge and independent work. We also discuss some regularization parameter alignment issues that come up in both approaches. Supplementary materials are available online.     

Sparse Penalized Forward Selection for Support Vector Classification

We propose a new binary classification and variable selection technique especially designed for high-dimensional predictors. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection along with classification. By adding an ?₁-type penalty to the loss function, common classification methods such as logistic regression or support vector machines (SVM) can perform variable selection. Existing penalized SVM methods all attempt to jointly solve all the parameters involved in the penalization problem altogether. When data dimension is very high, the joint optimization problem is very complex and involves a lot of memory allocation. In this article, we propose a new penalized forward search technique that can reduce high-dimensional optimization problems to one-dimensional optimization by iterating the selection steps. The new algorithm can be regarded as a forward selection version of the penalized SVM and its variants. The advantage of optimizing in one dimension is that the location of the optimum solution can be obtained with intelligent search by exploiting convexity and a piecewise linear or quadratic structure of the criterion function. In each step, the predictor that is most able to predict the outcome is chosen in the model. The search is then repeatedly used in an iterative fashion until convergence occurs. Comparison of our new classification rule with ?₁-SVM and other common methods show very promising performance, in that the proposed method leads to much leaner models without compromising misclassification rates, particularly for high-dimensional predictors.     

特征级联在车辆检测中的应用与研究

对现有的采用机器学习算法检测车辆进行研究,分析其存在的不足;表现在特征或者算法单一,对光照等条件变化鲁棒性不够;针对这些问题,提出一种融合LBP特征与HOG特征,并结合Adaboost与SVM的车辆检测算法;借鉴级联的思想,首先采用AdaBoost对训练样本提取LBP特征进行训练,得到的分类器用于初步筛选并将其分类结果作为下一层分类器的输入;然后采用SVM算法对训练样本提取HOG特征进行训练,得到的分类器用于二次筛选上一层分类器的分类结果;实验结果证明Adaboost-SVM相结合的办法检测结果精度高,准确率和召回率均达到95%以上,FPPW与FPPI的值均在5%左右;同时由于算法采用的特征对光照条件具有较强的鲁棒性,因此光照条件的变化对算法的识别结果影响较低;实时性方面,每帧图像的处理时间为75 ms,满足实时性要求。     

基于偏振光谱BRDF图像的物质分类

提出一种基于偏振光谱二向反射分布函数图像的物质自动分类方法,该方法主要选择偏振光谱二向反射分布函数信息作为新的特征用于物质自动分类.采用支撑向量机的分类方法对不同的天气条件（晴天、多云、阴天）下处于杂乱的自然草地背景环境中的典型目标进行分类,最后比较三种不同特征选择对于分类准确度的影响.采取三种不同的特征选取方法,分别为采用单一的光谱特征、偏振光谱特征及偏振光谱二向反射分布函数特征.最后通过实验得出：将偏振光谱二向反射分布函数作为分类特征在三种不同的天气情况下,分类准确度都较高,特别是在阴天天气条件下,分类准确度明显高于其它两种特征选择.即使是在阴天低照度下的场景中,当不同目标和背景之间的灰度很接近时,采用本文方法也能准确的进行自动分类.
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