双聚类
Spike(软件开发)
聚类分析
矩阵分解
计算机科学
算法
增广拉格朗日法
基质(化学分析)
扩展(谓词逻辑)
因式分解
非负矩阵分解
分块矩阵
模式识别(心理学)
人工智能
特征向量
相关聚类
物理
软件工程
复合材料
程序设计语言
CURE数据聚类算法
材料科学
量子力学
作者
Matteo Denitto,Manuele Bicego,Alessandro Farinelli,Mário A. T. Figueiredo
标识
DOI:10.1016/j.patcog.2017.07.021
摘要
Biclustering refers to the problem of simultaneously clustering the rows and columns of a given data matrix, with the goal of obtaining submatrices where the selected rows present a coherent behaviour in the selected columns, and vice-versa. To face this intrinsically difficult problem, we propose a novel generative model, where biclustering is approached from a sparse low-rank matrix factorization perspective. The main idea is to design a probabilistic model describing the factorization of a given data matrix in two other matrices, from which information about rows and columns belonging to the sought for biclusters can be obtained. One crucial ingredient in the proposed model is the use of a spike and slab sparsity-inducing prior, thus we term the approach spike and slab biclustering (SSBi). To estimate the parameters of the SSBi model, we propose an expectation-maximization (EM) algorithm, termed SSBiEM, which solves a low-rank factorization problem at each iteration, using a recently proposed augmented Lagrangian algorithm. Experiments with both synthetic and real data show that the SSBi approach compares favorably with the state-of-the-art.
科研通智能强力驱动
Strongly Powered by AbleSci AI