Ren Lu, Du Lan, Carin Lawrence, Dunson David B
Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.
Department of Statistical Science, Duke University, Durham, NC 27708, USA.
J Mach Learn Res. 2011 Jan;12(Jan):203-239.
A logistic stick-breaking process (LSBP) is proposed for non-parametric clustering of general spatially- or temporally-dependent data, imposing the belief that proximate data are more likely to be clustered together. The sticks in the LSBP are realized via multiple logistic regression functions, with shrinkage priors employed to favor contiguous and spatially localized segments. The LSBP is also extended for the simultaneous processing of multiple data sets, yielding a hierarchical logistic stick-breaking process (H-LSBP). The model parameters (atoms) within the H-LSBP are shared across the multiple learning tasks. Efficient variational Bayesian inference is derived, and comparisons are made to related techniques in the literature. Experimental analysis is performed for audio waveforms and images, and it is demonstrated that for segmentation applications the LSBP yields generally homogeneous segments with sharp boundaries.
本文提出了一种逻辑折断棒过程（LSBP），用于对一般的空间或时间相关数据进行非参数聚类，基于相邻数据更有可能聚类在一起的信念。LSBP中的棒通过多个逻辑回归函数实现，采用收缩先验来支持连续和空间局部化的段。LSBP还扩展到同时处理多个数据集，产生了分层逻辑折断棒过程（H-LSBP）。H-LSBP中的模型参数（原子）在多个学习任务之间共享。推导了有效的变分贝叶斯推理，并与文献中的相关技术进行了比较。对音频波形和图像进行了实验分析，结果表明，对于分割应用，LSBP通常能产生具有清晰边界的均匀段。
