33,485 research outputs found
On Optimizing Distributed Tucker Decomposition for Dense Tensors
The Tucker decomposition expresses a given tensor as the product of a small
core tensor and a set of factor matrices. Apart from providing data
compression, the construction is useful in performing analysis such as
principal component analysis (PCA)and finds applications in diverse domains
such as signal processing, computer vision and text analytics. Our objective is
to develop an efficient distributed implementation for the case of dense
tensors. The implementation is based on the HOOI (Higher Order Orthogonal
Iterator) procedure, wherein the tensor-times-matrix product forms the core
routine. Prior work have proposed heuristics for reducing the computational
load and communication volume incurred by the routine. We study the two metrics
in a formal and systematic manner, and design strategies that are optimal under
the two fundamental metrics. Our experimental evaluation on a large benchmark
of tensors shows that the optimal strategies provide significant reduction in
load and volume compared to prior heuristics, and provide up to 7x speed-up in
the overall running time.Comment: Preliminary version of the paper appears in the proceedings of
IPDPS'1
Pairwise meta-rules for better meta-learning-based algorithm ranking
In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset
A Dynamic I/O-Efficient Structure for One-Dimensional Top-k Range Reporting
We present a structure in external memory for "top-k range reporting", which
uses linear space, answers a query in O(lg_B n + k/B) I/Os, and supports an
update in O(lg_B n) amortized I/Os, where n is the input size, and B is the
block size. This improves the state of the art which incurs O(lg^2_B n)
amortized I/Os per update.Comment: In PODS'1
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