1 research outputs found
Bidirectional Loss Function for Label Enhancement and Distribution Learning
Label distribution learning (LDL) is an interpretable and general learning
paradigm that has been applied in many real-world applications. In contrast to
the simple logical vector in single-label learning (SLL) and multi-label
learning (MLL), LDL assigns labels with a description degree to each instance.
In practice, two challenges exist in LDL, namely, how to address the
dimensional gap problem during the learning process of LDL and how to exactly
recover label distributions from existing logical labels, i.e., Label
Enhancement (LE). For most existing LDL and LE algorithms, the fact that the
dimension of the input matrix is much higher than that of the output one is
alway ignored and it typically leads to the dimensional reduction owing to the
unidirectional projection. The valuable information hidden in the feature space
is lost during the mapping process. To this end, this study considers
bidirectional projections function which can be applied in LE and LDL problems
simultaneously. More specifically, this novel loss function not only considers
the mapping errors generated from the projection of the input space into the
output one but also accounts for the reconstruction errors generated from the
projection of the output space back to the input one. This loss function aims
to potentially reconstruct the input data from the output data. Therefore, it
is expected to obtain more accurate results. Finally, experiments on several
real-world datasets are carried out to demonstrate the superiority of the
proposed method for both LE and LDL