  The instability is shown in the existing methods of representation learning
based on Euclidean distance under a broad set of conditions. Furthermore, the
scarcity and high cost of labels prompt us to explore more expressive
representation learning methods which depends on the labels as few as possible.
To address these issues, the small-perturbation ideology is firstly introduced
on the representation learning model based on the representation probability
distribution. The positive small-perturbation information (SPI) which only
depend on two labels of each cluster is used to stimulate the representation
probability distribution and then two variant models are proposed to fine-tune
the expected representation distribution of RBM, namely, Micro-supervised
Disturbance GRBM (Micro-DGRBM) and Micro-supervised Disturbance RBM
(Micro-DRBM) models. The Kullback-Leibler (KL) divergence of SPI is minimized
in the same cluster to promote the representation probability distributions to
become more similar in Contrastive Divergence(CD) learning. In contrast, the KL
divergence of SPI is maximized in the different clusters to enforce the
representation probability distributions to become more dissimilar in CD
learning. To explore the representation learning capability under the
continuous stimulation of the SPI, we present a deep Micro-supervised
Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and
Micro-DRBM models and compare it with a similar deep structure which has not
any external stimulation. Experimental results demonstrate that the proposed
deep Micro-DL architecture shows better performance in comparison to the
baseline method, the most related shallow models and deep frameworks for
clustering.
