论文标题
k-摩恩用于噪声不敏感的多维特征学习
K-Means for Noise-Insensitive Multi-Dimensional Feature Learning
论文作者
论文摘要
许多测量模态通过探测逐个像素的对象像素（例如通过光声显微镜）在每个像素上产生多维特征（通常是时间域信号）。原则上，时间域信号中的许多自由度将承认隐含地存在重要的多模式信息的可能性，而不是单个标量的“亮度”，就观察到的基本目标而言。但是，测得的信号既不是基本函数的加权和加权和，也不是一组原型（K-均值）之一，它激发了此处提出的新型聚类方法。信号是根据其形状而不是振幅来簇的，而不是通过角度距离和质心来计算，将信号计算为最大群体内方差的方向，从而产生了一种能够学习质心（信号形状）的聚类算法，这些算法（信号形状）与潜在的，具有可扩展和噪声的目标特征相关，但具有潜在的目标特征。
Many measurement modalities which perform imaging by probing an object pixel-by-pixel, such as via Photoacoustic Microscopy, produce a multi-dimensional feature (typically a time-domain signal) at each pixel. In principle, the many degrees of freedom in the time-domain signal would admit the possibility of significant multi-modal information being implicitly present, much more than a single scalar "brightness", regarding the underlying targets being observed. However, the measured signal is neither a weighted-sum of basis functions (such as principal components) nor one of a set of prototypes (K-means), which has motivated the novel clustering method proposed here. Signals are clustered based on their shape, but not amplitude, via angular distance and centroids are calculated as the direction of maximal intra-cluster variance, resulting in a clustering algorithm capable of learning centroids (signal shapes) that are related to the underlying, albeit unknown, target characteristics in a scalable and noise-robust manner.
