机译:通过最大投影和最小冗余实现无监督特征选择
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China,Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G2G7, Canada;
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G2G7, Canada,System Research Institute, Polish Academy of Sciences, Warsaw, Poland;
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
Lab of Granular Computing, Minnan Normal University, Zhangzhou 363000, China;
Machine learning; Feature selection; Unsupervised learning; Matrix factorization; Kernel method; Minimum redundancy;
机译:基于频谱聚类的无监督特征选择,具有最大相关性和最小冗余方法
机译:基于最大信息和最小冗余的高光谱图像的无监督特征选择
机译:中位过滤取证的最大相关性和最小冗余功能选择方法
机译:使用最小冗余最大相关性的帕金森氏病步态冻结的足底压力和踝关节加速特征选择
机译:通过在无人监督的设置中进行功能选择来发现类。
机译:时间基因表达数据的最小冗余最大相关特征选择方法
机译:基于禁忌搜索帕金森病挖掘的改进的最大相关最小冗余特征选择方法
机译:改进的特征提取,特征选择和识别技术,创建快速无监督的高光谱目标检测算法