5,607 research outputs found
A Convex Formulation for Spectral Shrunk Clustering
Spectral clustering is a fundamental technique in the field of data mining
and information processing. Most existing spectral clustering algorithms
integrate dimensionality reduction into the clustering process assisted by
manifold learning in the original space. However, the manifold in
reduced-dimensional subspace is likely to exhibit altered properties in
contrast with the original space. Thus, applying manifold information obtained
from the original space to the clustering process in a low-dimensional subspace
is prone to inferior performance. Aiming to address this issue, we propose a
novel convex algorithm that mines the manifold structure in the low-dimensional
subspace. In addition, our unified learning process makes the manifold learning
particularly tailored for the clustering. Compared with other related methods,
the proposed algorithm results in more structured clustering result. To
validate the efficacy of the proposed algorithm, we perform extensive
experiments on several benchmark datasets in comparison with some
state-of-the-art clustering approaches. The experimental results demonstrate
that the proposed algorithm has quite promising clustering performance.Comment: AAAI201
Robust Motion Segmentation from Pairwise Matches
In this paper we address a classification problem that has not been
considered before, namely motion segmentation given pairwise matches only. Our
contribution to this unexplored task is a novel formulation of motion
segmentation as a two-step process. First, motion segmentation is performed on
image pairs independently. Secondly, we combine independent pairwise
segmentation results in a robust way into the final globally consistent
segmentation. Our approach is inspired by the success of averaging methods. We
demonstrate in simulated as well as in real experiments that our method is very
effective in reducing the errors in the pairwise motion segmentation and can
cope with large number of mismatches
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