35,096 research outputs found
Distributed Low-rank Subspace Segmentation
Vision problems ranging from image clustering to motion segmentation to
semi-supervised learning can naturally be framed as subspace segmentation
problems, in which one aims to recover multiple low-dimensional subspaces from
noisy and corrupted input data. Low-Rank Representation (LRR), a convex
formulation of the subspace segmentation problem, is provably and empirically
accurate on small problems but does not scale to the massive sizes of modern
vision datasets. Moreover, past work aimed at scaling up low-rank matrix
factorization is not applicable to LRR given its non-decomposable constraints.
In this work, we propose a novel divide-and-conquer algorithm for large-scale
subspace segmentation that can cope with LRR's non-decomposable constraints and
maintains LRR's strong recovery guarantees. This has immediate implications for
the scalability of subspace segmentation, which we demonstrate on a benchmark
face recognition dataset and in simulations. We then introduce novel
applications of LRR-based subspace segmentation to large-scale semi-supervised
learning for multimedia event detection, concept detection, and image tagging.
In each case, we obtain state-of-the-art results and order-of-magnitude speed
ups
DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks
Background and Objective: Heterogeneous complex networks are large graphs
consisting of different types of nodes and edges. The knowledge extraction from
these networks is complicated. Moreover, the scale of these networks is
steadily increasing. Thus, scalable methods are required. Methods: In this
paper, two distributed label propagation algorithms for heterogeneous networks,
namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type
of the heterogeneous complex networks. As a case study, we have measured the
efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network
consisting of drugs, diseases, and targets. The subject we have studied in this
network is drug repositioning but our algorithms can be used as general methods
for heterogeneous networks other than the biological network. Results: We
compared the proposed algorithms with similar non-distributed versions of them
namely MINProp and Heter-LP. The experiments revealed the good performance of
the algorithms in terms of running time and accuracy.Comment: Source code available for Apache Giraph on Hadoo
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