Title: Autoencoder Based Sample Selection for Self-Taught Learning,
Abstract: Self-taught learning is a technique that uses a large number of unlabeled
data as source samples to improve the task performance on target samples.
Compared with other transfer learning techniques, self-taught learning can be
applied to a broader set of scenarios due to the loose restrictions on source
data. However, knowledge transferred from source samples that are not
sufficiently related to the target domain may negatively influence the target
learner, which is referred to as negative transfer. In this paper, we propose a
metric for the relevance between a source sample and target samples. To be more
specific, both source and target samples are reconstructed through a
single-layer autoencoder with a linear relationship between source samples and
target samples simultaneously enforced. An l_{2,1}-norm sparsity constraint is
imposed on the transformation matrix to identify source samples relevant to the
target domain. Source domain samples that are deemed relevant are assigned
pseudo-labels reflecting their relevance to target domain samples, and are
combined with target samples in order to provide an expanded training set for
classifier training. Local data structures are also preserved during source
sample selection through spectral graph analysis. Promising results in
extensive experiments show the advantages of the proposed approach.