ICML 2017poster11 citations
Latent Feature Lasso
Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar
Abstract
The latent feature model (LFM), proposed in (Griffiths \& Ghahramani, 2005), but possibly with earlier origins, is a generalization of a mixture model, where each instance is generated not from a single latent class but from a combination of
BibTeX
@InProceedings{pmlr-v70-yen17a,
title = {Latent Feature Lasso},
author = {Ian En-Hsu Yen and Wei-Cheng Lee and Sung-En Chang and Arun Sai Suggala and Shou-De Lin and Pradeep Ravikumar},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {3949--3957},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/yen17a/yen17a.pdf},
url = {https://proceedings.mlr.press/v70/yen17a.html},
abstract = {The latent feature model (LFM), proposed in (Griffiths \& Ghahramani, 2005), but possibly with earlier origins, is a generalization of a mixture model, where each instance is generated not from a single latent class but from a combination of