7,904 research outputs found
Scalable Multi-view Clustering via Explicit Kernel Features Maps
A growing awareness of multi-view learning as an important component in data
science and machine learning is a consequence of the increasing prevalence of
multiple views in real-world applications, especially in the context of
networks. In this paper we introduce a new scalability framework for multi-view
subspace clustering. An efficient optimization strategy is proposed, leveraging
kernel feature maps to reduce the computational burden while maintaining good
clustering performance. The scalability of the algorithm means that it can be
applied to large-scale datasets, including those with millions of data points,
using a standard machine, in a few minutes. We conduct extensive experiments on
real-world benchmark networks of various sizes in order to evaluate the
performance of our algorithm against state-of-the-art multi-view subspace
clustering methods and attributed-network multi-view approaches
Neighborhood Selection for Thresholding-based Subspace Clustering
Subspace clustering refers to the problem of clustering high-dimensional data
points into a union of low-dimensional linear subspaces, where the number of
subspaces, their dimensions and orientations are all unknown. In this paper, we
propose a variation of the recently introduced thresholding-based subspace
clustering (TSC) algorithm, which applies spectral clustering to an adjacency
matrix constructed from the nearest neighbors of each data point with respect
to the spherical distance measure. The new element resides in an individual and
data-driven choice of the number of nearest neighbors. Previous performance
results for TSC, as well as for other subspace clustering algorithms based on
spectral clustering, come in terms of an intermediate performance measure,
which does not address the clustering error directly. Our main analytical
contribution is a performance analysis of the modified TSC algorithm (as well
as the original TSC algorithm) in terms of the clustering error directly.Comment: ICASSP 201
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