Meng Lu,Jianhua Z Huang,Xiaoning Qian
Meng Lu
We propose a Sparse exponential family Principal Component Analysis (SePCA) method suitable for any type of data following exponential family distributions, to achieve simultaneous dimension reduction and variable selection for better inter...
Chen Zu,Zhengxia Wang,Daoqiang Zhang et al.
Chen Zu et al.
Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a pa...
Cross-domain, soft-partition clustering with diversity measure and knowledge reference [0.03%]
基于多样性测度和知识参考的跨域软聚类方法
Pengjiang Qian,Shouwei Sun,Yizhang Jiang et al.
Pengjiang Qian et al.
Conventional, soft-partition clustering approaches, such as fuzzy c-means (FCM), maximum entropy clustering (MEC) and fuzzy clustering by quadratic regularization (FC-QR), are usually incompetent in those situations where the data are quite...
A continuous linear optimal transport approach for pattern analysis in image datasets [0.03%]
图像数据集中的连续线性最优传输模式分析方法
Soheil Kolouri,Akif B Tosun,John A Ozolek et al.
Soheil Kolouri et al.
We present a new approach to facilitate the application of the optimal transport metric to pattern recognition on image databases. The method is based on a linearized version of the optimal transport metric, which provides a linear embeddin...
Classifier Design Given an Uncertainty Class of Feature Distributions via Regularized Maximum Likelihood and the Incorporation of Biological Pathway Knowledge in Steady-State Phenotype Classification [0.03%]
基于特征分布不确定类的分类器设计及稳态表型分类中的生物通路知识融合
Mohammad Shahrokh Esfahani,Jason Knight,Amin Zollanvari et al.
Mohammad Shahrokh Esfahani et al.
Contemporary high-throughput technologies provide measurements of very large numbers of variables but often with very small sample sizes. This paper proposes an optimization-based paradigm for utilizing prior knowledge to design better perf...
A scale- and orientation-adaptive extension of Local Binary Patterns for texture classification [0.03%]
用于纹理分类的局部二值模式的尺度和方向自适应扩展方法
Sebastian Hegenbart,Andreas Uhl
Sebastian Hegenbart
Local Binary Patterns (LBPs) have been used in a wide range of texture classification scenarios and have proven to provide a highly discriminative feature representation. A major limitation of LBP is its sensitivity to affine transformation...
Cross-trees, Edge and Superpixel Priors-based Cost aggregation for Stereo matching [0.03%]
交叉树、边缘和超像素先验用于立体匹配的成本聚集
Feiyang Cheng,Hong Zhang,Mingui Sun et al.
Feiyang Cheng et al.
In this paper, we propose a novel cross-trees structure to perform the nonlocal cost aggregation strategy, and the cross-trees structure consists of a horizontal-tree and a vertical-tree. Compared to other spanning trees, the significant su...
Coupled Segmentation of Nuclear and Membrane-bound Macromolecules through Voting and Multiphase Level Set [0.03%]
基于投票和多相水平集的细胞核与膜结合大分子耦合分割方法
Hang Chang,Quan Wen,Bahram Parvin
Hang Chang
Membrane-bound macromolecules play an important role in tissue architecture and cell-cell communication, and is regulated by almost one-third of the genome. At the optical scale, one group of membrane proteins expresses themselves as linear...
Shijun Wang,Diana Li,Nicholas Petrick et al.
Shijun Wang et al.
Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separ...
Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model [0.03%]
高斯模型下贝叶斯最小均方误差分类错误估计的矩和均方根误差
Amin Zollanvari,Edward R Dougherty
Amin Zollanvari
The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because th...