TY - GEN
T1 - Heterogeneous-neighborhood-based multi-task local learning algorithms
AU - Zhang, Yu
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013/12
Y1 - 2013/12
N2 - All the existing multi-task local learning methods are defined on homogeneous neighborhood which consists of all data points from only one task. In this paper, different from existing methods, we propose local learning methods for multitask classification and regression problems based on heterogeneous neighborhood which is defined on data points from all tasks. Specifically, we extend the κ- nearest-neighbor classifier by formulating the decision function for each data point as a weighted voting among the neighbors from all tasks where the weights are task-specific. By defining a regularizer to enforce the task-specific weight matrix to approach a symmetric one, a regularized objective function is proposed and an efficient coordinate descent method is developed to solve it. For regression problems, we extend the kernel regression to multi-task setting in a similar way to the classification case. Experiments on some toy data and real-world datasets demonstrate the effectiveness of our proposed methods.
AB - All the existing multi-task local learning methods are defined on homogeneous neighborhood which consists of all data points from only one task. In this paper, different from existing methods, we propose local learning methods for multitask classification and regression problems based on heterogeneous neighborhood which is defined on data points from all tasks. Specifically, we extend the κ- nearest-neighbor classifier by formulating the decision function for each data point as a weighted voting among the neighbors from all tasks where the weights are task-specific. By defining a regularizer to enforce the task-specific weight matrix to approach a symmetric one, a regularized objective function is proposed and an efficient coordinate descent method is developed to solve it. For regression problems, we extend the kernel regression to multi-task setting in a similar way to the classification case. Experiments on some toy data and real-world datasets demonstrate the effectiveness of our proposed methods.
UR - https://proceedings.neurips.cc/paper/2013/file/8f468c873a32bb0619eaeb2050ba45d1-Paper.pdf
UR - https://proceedings.neurips.cc/paper/2013/hash/8f468c873a32bb0619eaeb2050ba45d1-Abstract.html
UR - http://www.scopus.com/inward/record.url?scp=84898928624&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84898928624
SN - 9781632660244
T3 - Advances in Neural Information Processing Systems
BT - 27th Annual Conference on Neural Information Processing Systems, NIPS 2013
A2 - Burges, C. J.
A2 - Bottou, L.
A2 - Welling, M.
A2 - Ghahramani, Z.
A2 - Weinberger, K.Q.
PB - Neural Information Processing Systems Foundation
T2 - 27th Annual Conference on Neural Information Processing Systems, NIPS 2013
Y2 - 5 December 2013 through 10 December 2013
ER -