  Multi-task clustering (MTC) has attracted a lot of research attentions in
machine learning due to its ability in utilizing the relationship among
different tasks. Despite the success of traditional MTC models, they are either
easy to stuck into local optima, or sensitive to outliers and noisy data. To
alleviate these problems, we propose a novel self-paced multi-task clustering
(SPMTC) paradigm. In detail, SPMTC progressively selects data examples to train
a series of MTC models with increasing complexity, thus highly decreases the
risk of trapping into poor local optima. Furthermore, to reduce the negative
influence of outliers and noisy data, we design a soft version of SPMTC to
further improve the clustering performance. The corresponding SPMTC framework
can be easily solved by an alternating optimization method. The proposed model
is guaranteed to converge and experiments on real data sets have demonstrated
its promising results compared with state-of-the-art multi-task clustering
methods.
