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Self-taught learning is a technique that uses a large number of unlabeleddata as source samples to improve the task performance on target samples.Compared with other transfer learning techniques, self-taught learning can beapplied to a broader set of scenarios due to the loose restrictions on thesource data. However, knowledge transferred from source samples that are notsufficiently related to the target domain may negatively influence the targetlearner, which is referred to as negative transfer. In this paper, we propose ametric for the relevance between a source sample and the target samples. To bemore specific, both source and target samples are reconstructed through asingle-layer autoencoder with a linear relationship between source samples andreconstructed target samples being simultaneously enforced. An$\ell_{2,1}$-norm sparsity constraint is imposed on the transformation matrixto identify source samples relevant to the target domain. Source domain samplesthat are deemed relevant are assigned pseudo-labels reflecting their relevanceto target domain samples, and are combined with target samples in order toprovide an expanded training set for classifier training. Local data structuresare also preserved during source sample selection through spectral graphanalysis. Promising results in extensive experiments show the advantages of theproposed approach.

Autoencoder Based Sample Selection for Self-Taught Learning