  Learning with the \textit{instance-dependent} label noise is challenging,
because it is hard to model such real-world noise. Note that there are
psychological and physiological evidences showing that we humans perceive
instances by decomposing them into parts. Annotators are therefore more likely
to annotate instances based on the parts rather than the whole instances, where
a wrong mapping from parts to classes may cause the instance-dependent label
noise. Motivated by this human cognition, in this paper, we approximate the
instance-dependent label noise by exploiting \textit{part-dependent} label
noise. Specifically, since instances can be approximately reconstructed by a
combination of parts, we approximate the instance-dependent \textit{transition
matrix} for an instance by a combination of the transition matrices for the
parts of the instance. The transition matrices for parts can be learned by
exploiting anchor points (i.e., data points that belong to a specific class
almost surely). Empirical evaluations on synthetic and real-world datasets
demonstrate our method is superior to the state-of-the-art approaches for
learning from the instance-dependent label noise.
