Abstrakti
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.
| Alkuperäiskieli | Englanti |
|---|---|
| Sivumäärä | 5 |
| Tila | Julkaistu - 2017 |
| OKM-julkaisutyyppi | Ei sovellu |
| Tapahtuma | ANNUAL CONFERENCE ON NEURAL INFORMATION PROCESSING SYSTEMS - Long Beach, Yhdysvallat Kesto: 4 jouluk. 2017 → 9 jouluk. 2017 Konferenssinumero: 31 |
Conference
| Conference | ANNUAL CONFERENCE ON NEURAL INFORMATION PROCESSING SYSTEMS |
|---|---|
| Lyhennettä | NIPS |
| Maa/Alue | Yhdysvallat |
| Kaupunki | Long Beach |
| Ajanjakso | 04/12/2017 → 09/12/2017 |