User attribute discovery with missing labels

作者：

Highlights：

• We propose a new problem, i.e., user attribute discovery via smart sensor data.

• We design a new a semi-supervised multi-task learning model (S2MTL) for user attribute discovery with missing label.

• To reduce the model complexity of high-dimensional data, we learn the mapping feature dictionary and attribute space information simultaneously.

• We also build a new smart building dataset.

摘要

•We propose a new problem, i.e., user attribute discovery via smart sensor data.•We design a new a semi-supervised multi-task learning model (S2MTL) for user attribute discovery with missing label.•To reduce the model complexity of high-dimensional data, we learn the mapping feature dictionary and attribute space information simultaneously.•We also build a new smart building dataset.

论文关键词：User attribute,Smart sensor,Multi-task learning,Semi-supervised learning,Missing labels,Low rank

论文评审过程：Received 31 December 2016, Revised 20 April 2017, Accepted 7 July 2017, Available online 20 July 2017, Version of Record 18 September 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.07.012