Abstract
A recently proposed learning algorithm for massive network-structured data sets
(big data over networks) is the network Lasso (nLasso), which extends the wellknown Lasso estimator from sparse models to network-structured datasets. Efficient implementations of the nLasso have been presented using modern convex optimization methods. In this paper we provide sufficient conditions on the network structure and available label information such that nLasso accurately learns a vector-valued graph signal (representing label information) from the information provided by the labels of a few data points.
(big data over networks) is the network Lasso (nLasso), which extends the wellknown Lasso estimator from sparse models to network-structured datasets. Efficient implementations of the nLasso have been presented using modern convex optimization methods. In this paper we provide sufficient conditions on the network structure and available label information such that nLasso accurately learns a vector-valued graph signal (representing label information) from the information provided by the labels of a few data points.
| Original language | English |
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| Number of pages | 5 |
| Publication status | Published - 2017 |
| MoE publication type | Not Eligible |
| Event | IEEE Conference on Neural Information Processing Systems - Long Beach, United States Duration: 4 Dec 2017 → 9 Dec 2017 Conference number: 31 |
Conference
| Conference | IEEE Conference on Neural Information Processing Systems |
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| Abbreviated title | NIPS |
| Country/Territory | United States |
| City | Long Beach |
| Period | 04/12/2017 → 09/12/2017 |