  Multitask learning (MTL) aims to learn multiple tasks simultaneously through
the interdependence between different tasks. The way to measure the relatedness
between tasks is always a popular issue. There are mainly two ways to measure
relatedness between tasks: common parameters sharing and common features
sharing across different tasks. However, these two types of relatedness are
mainly learned independently, leading to a loss of information. In this paper,
we propose a new strategy to measure the relatedness that jointly learns shared
parameters and shared feature representations. The objective of our proposed
method is to transform the features from different tasks into a common feature
space in which the tasks are closely related and the shared parameters can be
better optimized. We give a detailed introduction to our proposed multitask
learning method. Additionally, an alternating algorithm is introduced to
optimize the nonconvex objection. A theoretical bound is given to demonstrate
that the relatedness between tasks can be better measured by our proposed
multitask learning algorithm. We conduct various experiments to verify the
superiority of the proposed joint model and feature a multitask learning
method.
