Multidimensional Scaling by Deterministic Annealing with Iterative Majorization Algorithm

Bae, Seunghee; Qiu, Judy; Fox, Geoffrey

doi:10.1109/escience.2010.45

Cited by 9 publications

(9 citation statements)

References 27 publications

(43 reference statements)

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“…However, they suffer from long running times due to their Monte Carlo approach. DA-SMACOF [29] can reduce the time cost and find global optima by using deterministic annealing [30]. But DA-SMACOF assumes all weights are equal to one for all input distance matrices.…”

Section: Related Workmentioning

confidence: 99%

Integration of Clustering and Multidimensional Scaling to Determine Phylogenetic Trees as Spherical Phylograms Visualized in 3 Dimensions

Ruan¹,

House²,

Ekanayake³

et al. 2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Phylogenetic analysis is commonly used to analyze genetic sequence data from fungal communities, while ordination and clustering techniques commonly are used to analyze sequence data from bacterial communities. However, few studies have attempted to link these two independent approaches. In this paper, we propose a method, which we call spherical phylogram (SP), to display the phylogenetic tree within the clustering and visualization result from a pipeline called DACIDR. In comparison with traditional tree display methods, the correlations between the tree and the clustering can be observed directly. In addition, we propose an algorithm called interpolative joining (IJ) to construct and visualize the SP in 3D space. In the experiments, we used the sum of branch lengths to quantify the general fit between the clustering and the phylogenetic tree in SP and Mantel tests to determine how well the same grouping of sequences was preserved between the clustering and the SP. Our results show that DACIDR has a classification accuracy that is similar to a phylogenetic tree generated using a multiple sequence alignment, while having much lower computational cost.

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Section: Related Workmentioning

confidence: 99%

Integration of Clustering and Multidimensional Scaling to Determine Phylogenetic Trees as Spherical Phylograms Visualized in 3 Dimensions

Ruan¹,

House²,

Ekanayake³

et al. 2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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“…Full MDS includes weighted Deterministic Annealing (WDA), nonweighted Deterministic Annealing (NDA), weighted Expectation Maximization (WEM), and non-weighted Expectation Maximization (NEM) of SMACOF. Among them, WEM-and NEM-SMACOF are proposed in [6], NDA-SMACOF was proposed in [15]. Interpolation includes WDA, WEM, NDA, and NEM of MI-MDS.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, to find the majorizing function for (11), we apply (15) and (16) to (14). By using Cauchy-Schwarz inequality, the majorization inequality for the STRESS function is obtained as following…”

Section: Weighted Da-smacofmentioning

confidence: 99%

“…Additionally, as this method is an Expectation Maximization (EM) like problem, it is suffered from local optima problem. [15] has added a DA solution to SMACOF, so called DA-SMACOF, where it increased mapping quality and decreased the sensitivity with respect to initial configuration.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the nonlinear property of MDS problem, an EM-like optimization method called SMACOF is proposed to minimize the STRESS value in (1). And to overcome the local optima problem mentioned previously, [15] added a computational temperature to the SMACOF function, called DA-SMACOF. It has been proved to be reliable, fast, and robust without weighting.…”

Section: A Multidimensional Scalingmentioning

confidence: 99%

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A Robust and Scalable Solution for Interpolative Multidimensional Scaling with Weighting

Ruan

Fox

2013

2013 IEEE 9th International Conference on E-Science

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Abstract-Advances in modern bio-sequencing techniques have led to a proliferation of raw genomic data that enables an unprecedented opportunity for data mining. To analyze such large volume and high-dimensional scientific data, many high performance dimension reduction and clustering algorithms have been developed. Among the known algorithms, we use Multidimensional Scaling (MDS) to reduce the dimension of original data and Pairwise Clustering, and to classify the data. We have shown that an interpolative technique can be applied to get better performance on massive data. However, SMACOF MDS approach is only directly applicable to cases where all pairwise distances are used and where weight is one for each term. In this paper, we proposed a robust and scalable MDS and interpolation algorithm using Deterministic Annealing technique, to solve problems with either missing distances or a non-trivial weight function. We compared our method to three state-of-art techniques. By experimenting on three common types of bioinformatics dataset, the results illustrate that the precision of our algorithms are better than other algorithms, and the weighted solutions has a lower computational time cost as well.

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High‐performance iterative dataflow abstractions in Twister2:TSet

Wickramasinghe

Perera

Kamburugamuve

et al. 2020

Concurrency and Computation

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Summary The dataflow model is gradually becoming the de facto standard for big data applications. While many popular frameworks are built around this model, very little research has been done on understanding its inner workings, which in turn has led to inefficiencies in existing frameworks. It is important to note that understanding the relationship between dataflow and high performance computing (HPC) building blocks allows us to address and alleviate many of these fundamental inefficiencies by learning from the extensive research literature in the HPC community. In this article, we present TSets, the dataflow abstraction of Twister2, which is a big data framework designed for high‐performance dataflow and iterative computations. We discuss the dataflow model adopted by TSets and the rationale behind implementing iteration handling at the worker level. Finally, we evaluate TSets to show the performance of the framework and the importance of the worker level iteration model.

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Multidimensional Scaling by Deterministic Annealing with Iterative Majorization Algorithm

Cited by 9 publications

References 27 publications

Integration of Clustering and Multidimensional Scaling to Determine Phylogenetic Trees as Spherical Phylograms Visualized in 3 Dimensions

Integration of Clustering and Multidimensional Scaling to Determine Phylogenetic Trees as Spherical Phylograms Visualized in 3 Dimensions

A Robust and Scalable Solution for Interpolative Multidimensional Scaling with Weighting

High‐performance iterative dataflow abstractions in Twister2:TSet

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