2 research outputs found
Spectral Perturbation Meets Incomplete Multi-view Data
Beyond existing multi-view clustering, this paper studies a more realistic
clustering scenario, referred to as incomplete multi-view clustering, where a
number of data instances are missing in certain views. To tackle this problem,
we explore spectral perturbation theory. In this work, we show a strong link
between perturbation risk bounds and incomplete multi-view clustering. That is,
as the similarity matrix fed into spectral clustering is a quantity bounded in
magnitude O(1), we transfer the missing problem from data to similarity and
tailor a matrix completion method for incomplete similarity matrix. Moreover,
we show that the minimization of perturbation risk bounds among different views
maximizes the final fusion result across all views. This provides a solid
fusion criteria for multi-view data. We motivate and propose a
Perturbation-oriented Incomplete multi-view Clustering (PIC) method.
Experimental results demonstrate the effectiveness of the proposed method.Comment: to appear in IJCAI 201
SA-Net: A deep spectral analysis network for image clustering
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.Comment: arXiv admin note: text overlap with arXiv:2009.0523