TY - GEN
T1 - One-shot atomic detection
AU - Sun, Yifan
AU - Friedlander, Michael
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Feature selection in data science involves identifying the most prominent and uncorrelated features in the data, which can be useful for compression and interpretability. If these feature can be easily extracted, then a model can be trained over a reduced set of weights, which leads to more efficient training and possibly more robust classifiers. There are many approaches to feature selection; in this work, we propose screening the 'atoms' of a gradient of a loss function taken at a random point. We illustrate this approach on sparse and low-rank optimization problems. Despite the simplicity of the approach, we are often able to select the dominant features easily, and greatly improve the runtime and robustness in training overparametrized models.
AB - Feature selection in data science involves identifying the most prominent and uncorrelated features in the data, which can be useful for compression and interpretability. If these feature can be easily extracted, then a model can be trained over a reduced set of weights, which leads to more efficient training and possibly more robust classifiers. There are many approaches to feature selection; in this work, we propose screening the 'atoms' of a gradient of a loss function taken at a random point. We illustrate this approach on sparse and low-rank optimization problems. Despite the simplicity of the approach, we are often able to select the dominant features easily, and greatly improve the runtime and robustness in training overparametrized models.
UR - https://www.scopus.com/pages/publications/85082395348
U2 - 10.1109/CAMSAP45676.2019.9022441
DO - 10.1109/CAMSAP45676.2019.9022441
M3 - Conference contribution
AN - SCOPUS:85082395348
T3 - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
SP - 1
EP - 5
BT - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Y2 - 15 December 2019 through 18 December 2019
ER -