  Although supervised deep representation learning has attracted enormous
attentions across areas of pattern recognition and computer vision, little
progress has been made towards unsupervised deep representation learning for
image clustering. In this paper, we propose a deep spectral analysis network
for unsupervised representation learning and image clustering. While spectral
analysis is established with solid theoretical foundations and has been widely
applied to unsupervised data mining, its essential weakness lies in the fact
that it is difficult to construct a proper affinity matrix and determine the
involving Laplacian matrix for a given dataset. In this paper, we propose a
SA-Net to overcome these weaknesses and achieve improved image clustering by
extending the spectral analysis procedure into a deep learning framework with
multiple layers. The SA-Net has the capability to learn deep representations
and reveal deep correlations among data samples. Compared with the existing
spectral analysis, the SA-Net achieves two advantages: (i) Given the fact that
one spectral analysis procedure can only deal with one subset of the given
dataset, our proposed SA-Net elegantly integrates multiple parallel and
consecutive spectral analysis procedures together to enable interactive
learning across different units towards a coordinated clustering model; (ii)
Our SA-Net can identify the local similarities among different images at patch
level and hence achieves a higher level of robustness against occlusions.
Extensive experiments on a number of popular datasets support that our proposed
SA-Net outperforms 11 benchmarks across a number of image clustering
applications.
