Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Bickel P. J."'
A new approach to the sparse Canonical Correlation Analysis (sCCA)is proposed with the aim of discovering interpretable associations in very high-dimensional multi-view, i.e.observations of multiple sets of variables on the same subjects, problems. I
Externí odkaz:
http://arxiv.org/abs/1909.07947
Autor:
Li, Tianxi, Lei, Lihua, Bhattacharyya, Sharmodeep, Berge, Koen Van den, Sarkar, Purnamrita, Bickel, Peter J., Levina, Elizaveta
The problem of community detection in networks is usually formulated as finding a single partition of the network into some "correct" number of communities. We argue that it is more interpretable and in some regimes more accurate to construct a hiera
Externí odkaz:
http://arxiv.org/abs/1810.01509
In this article, we advance divide-and-conquer strategies for solving the community detection problem in networks. We propose two algorithms which perform clustering on a number of small subgraphs and finally patches the results into a single cluster
Externí odkaz:
http://arxiv.org/abs/1708.05573
We focus on spectral clustering of unlabeled graphs and review some results on clustering methods which achieve weak or strong consistent identification in data generated by such models. We also present a new algorithm which appears to perform optima
Externí odkaz:
http://arxiv.org/abs/1508.01819
The study of networks has received increased attention recently not only from the social sciences and statistics but also from physicists, computer scientists and mathematicians. One of the principal problem in networks is community detection. Many a
Externí odkaz:
http://arxiv.org/abs/1401.3915
Autor:
Bickel, Peter J., Sarkar, Purnamrita
Community detection in networks is a key exploratory tool with applications in a diverse set of areas, ranging from finding communities in social and biological networks to identifying link farms in the World Wide Web. The problem of finding communit
Externí odkaz:
http://arxiv.org/abs/1311.2694
Autor:
Sarkar, Purnamrita, Bickel, Peter J.
Publikováno v:
Annals of Statistics 2015, Vol. 43, No. 3, 962-990
Spectral clustering is a technique that clusters elements using the top few eigenvectors of their (possibly normalized) similarity matrix. The quality of spectral clustering is closely tied to the convergence properties of these principal eigenvector
Externí odkaz:
http://arxiv.org/abs/1310.1495
Publikováno v:
Annals of Statistics 2013, Vol. 41, No. 4, 2097-2122
Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse networks. Here we propose a new fast pseudo-likelihood method for fitting the stochastic bl
Externí odkaz:
http://arxiv.org/abs/1207.2340
Autor:
Song, Song, Bickel, Peter J.
One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor m
Externí odkaz:
http://arxiv.org/abs/1106.3915
Autor:
Chen, Aiyou, Bickel, Peter J.
Publikováno v:
Annals of Statistics 2006, Vol. 34, No. 6, 2825-2855
Independent component analysis (ICA) has been widely used for blind source separation in many fields such as brain imaging analysis, signal processing and telecommunication. Many statistical techniques based on M-estimates have been proposed for esti
Externí odkaz:
http://arxiv.org/abs/0705.4230