Sparse Additive Machine

Tuo Zhao, Han Liu
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1435-1443, 2012.

Abstract

We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machines (SVM) combined with sparse additive modeling. SAM is related to multiple kernel learning (MKL), but is computationally more efficient and amenable to theoretical analysis. In terms of computation, we develop an efficient accelerated proximal gradient descent algorithm which is also scalable to large data sets with a provable O(1/k^2) convergence rate and k is the number of iterations. In terms of theory, we provide the oracle properties of SAM under asymptotic frameworks. Empirical results on3 both synthetic and real data are reported to back up our theory.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-zhao12, title = {Sparse Additive Machine}, author = {Zhao, Tuo and Liu, Han}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {1435--1443}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/zhao12/zhao12.pdf}, url = {https://proceedings.mlr.press/v22/zhao12.html}, abstract = {We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machines (SVM) combined with sparse additive modeling. SAM is related to multiple kernel learning (MKL), but is computationally more efficient and amenable to theoretical analysis. In terms of computation, we develop an efficient accelerated proximal gradient descent algorithm which is also scalable to large data sets with a provable O(1/k^2) convergence rate and k is the number of iterations. In terms of theory, we provide the oracle properties of SAM under asymptotic frameworks. Empirical results on3 both synthetic and real data are reported to back up our theory.} }
Endnote
%0 Conference Paper %T Sparse Additive Machine %A Tuo Zhao %A Han Liu %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-zhao12 %I PMLR %P 1435--1443 %U https://proceedings.mlr.press/v22/zhao12.html %V 22 %X We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machines (SVM) combined with sparse additive modeling. SAM is related to multiple kernel learning (MKL), but is computationally more efficient and amenable to theoretical analysis. In terms of computation, we develop an efficient accelerated proximal gradient descent algorithm which is also scalable to large data sets with a provable O(1/k^2) convergence rate and k is the number of iterations. In terms of theory, we provide the oracle properties of SAM under asymptotic frameworks. Empirical results on3 both synthetic and real data are reported to back up our theory.
RIS
TY - CPAPER TI - Sparse Additive Machine AU - Tuo Zhao AU - Han Liu BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-zhao12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 1435 EP - 1443 L1 - http://proceedings.mlr.press/v22/zhao12/zhao12.pdf UR - https://proceedings.mlr.press/v22/zhao12.html AB - We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machines (SVM) combined with sparse additive modeling. SAM is related to multiple kernel learning (MKL), but is computationally more efficient and amenable to theoretical analysis. In terms of computation, we develop an efficient accelerated proximal gradient descent algorithm which is also scalable to large data sets with a provable O(1/k^2) convergence rate and k is the number of iterations. In terms of theory, we provide the oracle properties of SAM under asymptotic frameworks. Empirical results on3 both synthetic and real data are reported to back up our theory. ER -
APA
Zhao, T. & Liu, H.. (2012). Sparse Additive Machine. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:1435-1443 Available from https://proceedings.mlr.press/v22/zhao12.html.

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