首页>
外国专利>
SOURCE IDENTIFICATION BY NON-NEGATIVE MATRIX FACTORIZATION COMBINED WITH SEMI-SUPERVISED CLUSTERING
SOURCE IDENTIFICATION BY NON-NEGATIVE MATRIX FACTORIZATION COMBINED WITH SEMI-SUPERVISED CLUSTERING
展开▼
机译:非负矩阵分解与半监督聚类相结合的源识别
展开▼
页面导航
摘要
著录项
相似文献
摘要
Machine-learning methods and apparatus are provided to solve blind source separation problems with an unknown number of sources and having a signal propagation model with features such as wave-like propagation, medium-dependent velocity, attenuation, diffusion, and/or advection, between sources and sensors. In exemplary embodiments, multiple trials of non-negative matrix factorization are performed for a fixed number of sources, with selection criteria applied to determine successful trials. A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction quality and cluster separation. The number of sources is determined by comparing these measures for different trial numbers of sources. Source locations and parameters of the signal propagation model can also be determined. Disclosed methods are applicable to a wide range of spatial problems including chemical dispersal, pressure transients, and electromagnetic signals, and also to non-spatial problems such as cancer mutation.
展开▼