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EPrasanna Venkatesan, Sri Ramakrishnan, Balakrishnan Kannan, Aarathy Kannan
2017 Journal of Ophthalmic & Vision Research  
doi:10.4103/2008-322x.200165 pmid:28299020 pmcid:PMC5340056 fatcat:7vpnpotov5achafua7hboeutmy

Parallel Nonnegative CP Decomposition of Dense Tensors [article]

Grey Ballard and Koby Hayashi and Ramakrishnan Kannan
2018 arXiv   pre-print
The CP tensor decomposition is a low-rank approximation of a tensor. We present a distributed-memory parallel algorithm and implementation of an alternating optimization method for computing a CP decomposition of dense tensor data that can enforce nonnegativity of the computed low-rank factors. The principal task is to parallelize the matricized-tensor times Khatri-Rao product (MTTKRP) bottleneck subcomputation. The algorithm is computation efficient, using dimension trees to avoid redundant
more » ... avoid redundant computation across MTTKRPs within the alternating method. Our approach is also communication efficient, using a data distribution and parallel algorithm across a multidimensional processor grid that can be tuned to minimize communication. We benchmark our software on synthetic as well as hyperspectral image and neuroscience dynamic functional connectivity data, demonstrating that our algorithm scales well to 100s of nodes (up to 4096 cores) and is faster and more general than the currently available parallel software.
arXiv:1806.07985v1 fatcat:qx6g7qsrxvfsll2hu464cdvqsi

Outlier Detection for Text Data : An Extended Version [article]

Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park
2017 arXiv   pre-print
The problem of outlier detection is extremely challenging in many domains such as text, in which the attribute values are typically non-negative, and most values are zero. In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data. In this paper, we present a matrix factorization method, which is naturally able to distinguish the anomalies with the use of low rank approximations of the underlying data. Our iterative
more » ... Our iterative algorithm TONMF is based on block coordinate descent (BCD) framework. We define blocks over the term-document matrix such that the function becomes solvable. Given most recently updated values of other matrix blocks, we always update one block at a time to its optimal. Our approach has significant advantages over traditional methods for text outlier detection. Finally, we present experimental results illustrating the effectiveness of our method over competing methods.
arXiv:1701.01325v1 fatcat:ujl4qkjqdjasdosoprxbuhztv4

COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

Rathimala Kannan, Ivan Zhi Wei Wang, Hway Boon Ong, Kannan Ramakrishnan, Andry Alamsyah
2021 F1000Research  
The Malaysian government reacted to the pandemic's economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These
more » ... assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled "Effects of COVID-19 on the Economy and Individual - Round 2," collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques—Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes—were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance. Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.
doi:10.12688/f1000research.72976.1 pmid:34925768 pmcid:PMC8647044 fatcat:z62ysh7hu5eg3eajtq52i6e35m

A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization [article]

Ramakrishnan Kannan and Grey Ballard and Haesun Park
2015 arXiv   pre-print
Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors W and H, for the given input matrix A, such that A ≈ W H. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel software to solve the problem for big datasets. Existing
more » ... atasets. Existing distributed-memory algorithms are limited in terms of performance and applicability, as they are implemented using Hadoop and are designed only for sparse matrices. We propose a distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems for W and H. To our knowledge, our algorithm is the first high-performance parallel algorithm for NMF. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). As opposed to previous implementations, our algorithm is also flexible: (1) it performs well for dense and sparse matrices, and (2) it allows the user to choose from among multiple algorithms for solving local NLS subproblems within the alternating iterations. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements.
arXiv:1509.09313v1 fatcat:uhxr5bd73berxotxgrhdrdpofm

Job characteristics of a Malaysian bank's anti-money laundering system and its employees' job satisfaction

Rathimala Kannan, Yonesh Reddiar, Kannan Ramakrishnan, Marrynal S Eastaff, Shobana Ramesh
2021 F1000Research  
Data availability Underlying data Figshare: AML employees Job satisfaction -Raw Data. https://doi.org/10.6084/m9.figshare.15000003.v3 (Kannan and Reddiar, 2021) .  ... 
doi:10.12688/f1000research.73234.1 fatcat:stgb4g534rfivhgd5l43h3toei

Bounded Matrix Low Rank Approximation [chapter]

Ramakrishnan Kannan, Mariya Ishteva, Barry Drake, Haesun Park
2015 Signals and Communication Technology  
Matrix lower rank approximations such as nonnegative matrix factorization (NMF) have been successfully used to solve many data mining tasks. In this paper, we propose a new matrix lower rank approximation called Bounded Matrix Low Rank Approximation (BMA) which imposes a lower and an upper bound on every element of a lower rank matrix that best approximates a given matrix with missing elements. This new approximation models many real world problems, such as recommender systems, and performs
more » ... s, and performs better than other methods, such as singular value decompositions (SVD) or NMF. We present an efficient algorithm to solve BMA based on coordinate descent method. BMA is different from NMF as it imposes bounds on the approximation itself rather than on each of the low rank factors. We show that our algorithm is scalable for large matrices with missing elements on multi core systems with low memory. We present substantial experimental results illustrating that the proposed method outperforms the state of the art algorithms for recommender systems such as Stochastic Gradient Descent, Alternating Least Squares with regularization, SVD++, Bias-SVD on real world data sets such as Jester, Movielens, Book crossing, Online dating and Netflix.
doi:10.1007/978-3-662-48331-2_4 fatcat:wxtdcaqrqza6fkvut3ynmcbtu4

Behavioral clusters in dynamic graphs

James P. Fairbanks, Ramakrishnan Kannan, Haesun Park, David A. Bader
2015 Parallel Computing  
This paper contributes a method for combining sparse parallel graph algorithms with dense parallel linear algebra algorithms in order to understand dynamic graphs including the temporal behavior of vertices. Our method is the first to cluster vertices in a dynamic graph based on arbitrary temporal behaviors. In order to successfully implement this method, we develop a feature based pipeline for dynamic graphs and apply Nonnegative Matrix Factorization (NMF) to these features. We demonstrate
more » ... We demonstrate these steps with a sample of the Twitter mentions graph as well as a CAIDA network traffic graph.We contribute and analyze a parallel NMF algorithm presenting both theoretical and empirical studies of performance. This work can be leveraged by graph/network analysts to understand the temporal behavior cluster structure and segmentation structure of dynamic graphs.
doi:10.1016/j.parco.2015.03.002 fatcat:6jh5pyamxjerdnr4dsfkxded4m

MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization [article]

Ramakrishnan Kannan, Grey Ballard, Haesun Park
2016 arXiv   pre-print
Dataset A preliminary version of this work has already appeared as a conference paper [Kannan et al. 2016] .  ...  Theoretical and practical evidence supporting the first observation is also reported in our previous paper [Kannan et al. 2016 ].  ... 
arXiv:1609.09154v1 fatcat:xtcsszubtbeafnffj4ogr7wppq

Optimizing local control in anorectal melanoma

AS Ramakrishnan, V Mahajan, R Kannan
2008 Indian Journal of Cancer  
Patient 5 underwent re-excision of residual disease Ramakrishnan, et al.: Local control in anorectal melanoma Figure 1 : 1 Immunohistochemical staining (white arrow) in anorectal melanoma: A) S-100p  ...  recurrence recurrence recurrence Total Isolated WLE 5 4 2 2 2 WLE + RT 3 Nil Nil Nil 2 APR 3 Nil Nil 2 2 *Combination of sites is possible Ramakrishnan, et al.: Local control  ... 
doi:10.4103/0019-509x.40641 fatcat:ydtwlckflrbo7asj6stab4slne

PLANC: Parallel Low Rank Approximation with Non-negativity Constraints [article]

Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Michael A. Matheson, Haesun Park
2019 arXiv   pre-print
We consider the problem of low-rank approximation of massive dense non-negative tensor data, for example to discover latent patterns in video and imaging applications. As the size of data sets grows, single workstations are hitting bottlenecks in both computation time and available memory. We propose a distributed-memory parallel computing solution to handle massive data sets, loading the input data across the memories of multiple nodes and performing efficient and scalable parallel algorithms
more » ... arallel algorithms to compute the low-rank approximation. We present a software package called PLANC (Parallel Low Rank Approximation with Non-negativity Constraints), which implements our solution and allows for extension in terms of data (dense or sparse, matrices or tensors of any order), algorithm (e.g., from multiplicative updating techniques to alternating direction method of multipliers), and architecture (we exploit GPUs to accelerate the computation in this work).We describe our parallel distributions and algorithms, which are careful to avoid unnecessary communication and computation, show how to extend the software to include new algorithms and/or constraints, and report efficiency and scalability results for both synthetic and real-world data sets.
arXiv:1909.01149v1 fatcat:6lqh5upkujf3fpbd5wucc6wd24

A quantitative metabolomics peek into planarian regeneration

Nivedita Natarajan, Padma Ramakrishnan, Vairavan Lakshmanan, Dasaradhi Palakodeti, Kannan Rangiah
2015 The Analyst  
Methods were developed for the absolute quantification of metabolites from intact, regenerating planaria and from the day 3 blastema.
doi:10.1039/c4an02037e pmid:25815385 fatcat:gmrlh52b5je3nlpuqfgipb5pze

Vision loss in Guillain-Barre syndrome: Is it a complication of Guillain-Barre syndrome or just a coincidence?

Sri Ramakrishnan, Balakrishnan Kannan, Aarathy Kannan, EPrasanna Venkatesan
2016 Journal of Ophthalmic & Vision Research  
Sri Ramakrishnan 1 , DM; Balakrishnan Kannan 1 , DM; Aarathy Kannan 2 , MD; E.  ... 
doi:10.4103/2008-322x.188405 pmid:27621799 pmcid:PMC5000544 fatcat:lpth273zjva2xjlyaokb6hduhm

NIMBLE

Amol Ghoting, Prabhanjan Kambadur, Edwin Pednault, Ramakrishnan Kannan
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
In the last decade, advances in data collection and storage technologies have led to an increased interest in designing and implementing large-scale parallel algorithms for machine learning and data mining (ML-DM). Existing programming paradigms for expressing large-scale parallelism such as MapReduce (MR) and the Message Passing Interface (MPI) have been the de facto choices for implementing these ML-DM algorithms. The MR programming paradigm has been of particular interest as it gracefully
more » ... as it gracefully handles large datasets and has built-in resilience against failures. However, the existing parallel programming paradigms are too low-level and ill-suited for implementing ML-DM algorithms. To address this deficiency, we present NIMBLE, a portable infrastructure that has been specifically designed to enable the rapid implementation of parallel ML-DM algorithms. The infrastructure allows one to compose parallel ML-DM algorithms using reusable (serial and parallel) building blocks that can be efficiently executed using MR and other parallel programming models; it currently runs on top of Hadoop, which is an open-source MR implementation. We show how NIMBLE can be used to realize scalable implementations of ML-DM algorithms and present a performance evaluation.
doi:10.1145/2020408.2020464 dblp:conf/kdd/GhotingKPK11 fatcat:zxq6lotbj5dnpewebmftl6kjlu

Bounded Matrix Low Rank Approximation

Ramakrishnan Kannan, Mariya Ishteva, Haesun Park
2012 2012 IEEE 12th International Conference on Data Mining  
Matrix lower rank approximations such as nonnegative matrix factorization (NMF) have been successfully used to solve many data mining tasks. In this paper, we propose a new matrix lower rank approximation called Bounded Matrix Low Rank Approximation (BMA) which imposes a lower and an upper bound on every element of a lower rank matrix that best approximates a given matrix with missing elements. This new approximation models many real world problems, such as recommender systems, and performs
more » ... s, and performs better than other methods, such as singular value decompositions (SVD) or NMF. We present an efficient algorithm to solve BMA based on coordinate descent method. BMA is different from NMF as it imposes bounds on the approximation itself rather than on each of the low rank factors. We show that our algorithm is scalable for large matrices with missing elements on multi core systems with low memory. We present substantial experimental results illustrating that the proposed method outperforms the state of the art algorithms for recommender systems such as Stochastic Gradient Descent, Alternating Least Squares with regularization, SVD++, Bias-SVD on real world data sets such as Jester, Movielens, Book crossing, Online dating and Netflix.
doi:10.1109/icdm.2012.131 dblp:conf/icdm/KannanIP12 fatcat:bt2i6dfhwjcnvioc6fmgo4k4xu
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