499 research outputs found
Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation
Myriad of graph-based algorithms in machine learning and data mining require
parsing relational data iteratively. These algorithms are implemented in a
large-scale distributed environment in order to scale to massive data sets. To
accelerate these large-scale graph-based iterative computations, we propose
delta-based accumulative iterative computation (DAIC). Different from
traditional iterative computations, which iteratively update the result based
on the result from the previous iteration, DAIC updates the result by
accumulating the "changes" between iterations. By DAIC, we can process only the
"changes" to avoid the negligible updates. Furthermore, we can perform DAIC
asynchronously to bypass the high-cost synchronous barriers in heterogeneous
distributed environments. Based on the DAIC model, we design and implement an
asynchronous graph processing framework, Maiter. We evaluate Maiter on local
cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves
as much as 60x speedup over Hadoop and outperforms other state-of-the-art
frameworks.Comment: ScienceCloud 2012, TKDE 201
The spectrum of semi-Cayley graphs over abelian groups
AbstractIn this paper, a formula of the spectrum of semi-Cayley graphs over finite abelian groups will be given. In particular, the spectrum of Cayley graphs over dihedral groups and dicyclic groups will be given, respectively
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis
The probabilistic principal component analysis (PPCA) is built upon a global
linear mapping, with which it is insufficient to model complex data variation.
This paper proposes a mixture of bilateral-projection probabilistic principal
component analysis model (mixB2DPPCA) on 2D data. With multi-components in the
mixture, this model can be seen as a soft cluster algorithm and has capability
of modeling data with complex structures. A Bayesian inference scheme has been
proposed based on the variational EM (Expectation-Maximization) approach for
learning model parameters. Experiments on some publicly available databases
show that the performance of mixB2DPPCA has been largely improved, resulting in
more accurate reconstruction errors and recognition rates than the existing
PCA-based algorithms
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