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1
June 2017
HPDC '17: Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 25, Downloads (12 Months): 25, Downloads (Overall): 25
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k-means is one of the most influential and utilized machine learning algorithms. Its computation limits the performance and scalability of many statistical analysis and machine learning tasks. We rethink and optimize k-means in terms of modern NUMA architectures to develop a novel parallelization scheme that delays and minimizes synchronization barriers. ...
Keywords:
k-means, cloud, numa, parallel, clustering, semi-external memory
2
June 2017
Pattern Recognition Letters: Volume 92 Issue C, June 2017
Publisher: Elsevier Science Inc.
We propose a new manifold matching method, that is superior than existing methods based on single modality.Our method is robust against noise and different types of geometry in matching.The method is particularly useful for graph and network matching. Matching datasets of multiple modalities has become an important task in data ...
Keywords:
Seeded graph matching, k-nearest-neighbor, Geodesic distance, Nonlinear transformation
3
May 2017
IEEE Transactions on Parallel and Distributed Systems: Volume 28 Issue 5, May 2017
Publisher: IEEE Press
Sparse matrix multiplication is traditionally performed in memory and scales to large matrices using the distributed memory of multiple nodes. In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i.e., we keep the sparse matrix ...
4
January 2017
Pattern Recognition Letters: Volume 86 Issue C, January 2017
Publisher: Elsevier Science Inc.
The proposed model generalizes the traditional linear dynamical system model.An efficient algorithm is designed for parameter estimation.The model works very well on high-dimensional neuro-imaging data.It is also a standard tool for big time-series data analysis in many domains. High-dimensional time-series data from a wide variety of domains, such as neuroscience, ...
Keywords:
High dimension, State-space model, Time series analysis, Image processing, Parameter estimation
5
March 2016
IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 38 Issue 3, March 2016
Publisher: IEEE Computer Society
For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classification is asymptotically Bayes optimal; but this approach requires knowledge of and critically depends on the model dimension. In this paper, we propose a sparse representation vertex classifier ...
6
February 2016
ACM Transactions on Knowledge Discovery from Data (TKDD): Volume 10 Issue 3, February 2016
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 19, Downloads (12 Months): 156, Downloads (Overall): 233
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How much has a network changed since yesterday? How different is the wiring of Bob’s brain (a left-handed male) and Alice’s brain (a right-handed female), and how is it different? Graph similarity with given node correspondence, i.e., the detection of changes in the connectivity of graphs, arises in numerous settings. ...
Keywords:
graph classification, Graph similarity, culprit nodes and edges, graph comparison, node attribution, anomaly detection, edge attribution, network monitoring
7
January 2016
IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 38 Issue 1, January 2016
Publisher: IEEE Computer Society
Graph matching—aligning a pair of graphs to minimize their edge disagreements—has received wide-spread attention from both theoretical and applied communities over the past several decades, including combinatorics, computer vision, and connectomics. Its attention can be partially attributed to its computational difficulty. Although many heuristics have previously been proposed in the ...
8
August 2015
Parallel Computing: Volume 47 Issue C, August 2015
Publisher: Elsevier Science Publishers B. V.
We present a novel divide-and-conquer bijective graph matching algorithm.The algorithm is fully parallelizable, and scales to match "big data" graphs.We demonstrate the effectiveness of the algorithm by matching DTMRI human connectomes. We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. ...
Keywords:
Graph matching, Stochastic block model, Adjacency spectral embedding, Clustering
9
April 2015
Journal of Classification: Volume 32 Issue 1, April 2015
Publisher: Springer-Verlag New York, Inc.
We develop a formalism to address statistical pattern recognition of graph valued data. Of particular interest is the case of all graphs having the same number of uniquely labeled vertices. When the vertex labels are latent, such graphs are called shuffled graphs. Our formalism provides insight to trivially answer a ...
Keywords:
Connectomics, Random graphs, Graph matching, Statistical pattern recognition
10
February 2015
FAST'15: Proceedings of the 13th USENIX Conference on File and Storage Technologies
Publisher: USENIX Association
Graph analysis performs many random reads and writes, thus, these workloads are typically performed in memory. Traditionally, analyzing large graphs requires a cluster of machines so the aggregate memory exceeds the graph size. We demonstrate that a multicore server can process graphs with billions of vertices and hundreds of billions ...
11
December 2014
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 3, Downloads (12 Months): 19, Downloads (Overall): 30
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Throughout its history, humankind has been fascinated by a question that is simple to pose, yet remarkably resistant to resolution: "How does the brain work?" Philosophers have debated the workings of the mind for centuries. Da Vinci made detailed sketches of the brain. By the turn of the century, scientists ...
12
December 2013
NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0
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With simultaneous measurements from ever increasing populations of neurons, there is a growing need for sophisticated tools to recover signals from individual neurons. In electrophysiology experiments, this classically proceeds in a two-step process: (i) threshold the waveforms to detect putative spikes and (ii) cluster the waveforms into single units (neurons). ...
13
December 2013
NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0, Downloads (12 Months): 0, Downloads (Overall): 0
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Nonparametric estimation of the conditional distribution of a response given high-dimensional features is a challenging problem. It is important to allow not only the mean but also the variance and shape of the response density to change flexibly with features, which are massive-dimensional. We propose a multiscale dictionary learning model, ...
14
December 2013
NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 0, Downloads (12 Months): 0, Downloads (Overall): 0
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Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose a robust graph matching algorithm inspired in sparsity-related techniques. We cast the problem, resembling group or collaborative sparsity formulations, as a non-smooth ...
15
October 2013
BHI 2013: Proceedings of the International Conference on Brain and Health Informatics - Volume 8211
Publisher: Springer-Verlag New York, Inc.
The human brain and the neuronal networks comprising it are of immense interest to the scientific community. In this work, we focus on the structural connectivity of human brains, investigating sex differences across male and female connectomes (brain-graphs) for the knowledge discovery problem " Which brain regions exert differences in ...
Keywords:
sex classification, graph measures, network connectivity, pars orbitalis, human connectome, network science
16
October 2013
Machine Vision and Applications: Volume 24 Issue 7, October 2013
Publisher: Springer-Verlag New York, Inc.
It is widely believed that human brain is a complicated network and many neurological disorders such as Alzheimer's disease (AD) are related to abnormal changes of the brain network architecture. In this work, we present a kernel-based method to establish a network for each subject using mean cortical thickness, which ...
Keywords:
Cortical thickness, Alzheimer's disease, Classification, Network
17
Randal Burns,
Kunal Lillaney,
Daniel R. Berger,
Logan Grosenick,
Karl Deisseroth,
R. Clay Reid,
William Gray Roncal,
Priya Manavalan,
Davi D. Bock,
Narayanan Kasthuri,
Michael Kazhdan,
Stephen J. Smith,
Dean Kleissas,
Eric Perlman,
Kwanghun Chung,
Nicholas C. Weiler,
Jeff Lichtman,
Alexander S. Szalay,
Joshua T. Vogelstein,
R. Jacob Vogelstein
July 2013
SSDBM: Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 4, Downloads (12 Months): 41, Downloads (Overall): 250
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We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes ---neural connectivity maps of the brain---using the parallel ...
Keywords:
connectomics, data-intensive computing
18
July 2013
IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 35 Issue 7, July 2013
Publisher: IEEE Computer Society
This manuscript considers the following “graph classification” question: Given a collection of graphs and associated classes, how can one predict the class of a newly observed graph? To address this question, we propose a statistical model for graph/class pairs. This model naturally leads to a set of estimators to identify ...
Keywords:
Pattern analysis,Joints,Neurons,Analytical models,Brain modeling,Training,Data models,classification,Statistical inference,graph theory,network theory,structural pattern recognition,connectome
19
September 2011
MLMI'11: Proceedings of the Second international conference on Machine learning in medical imaging
Publisher: Springer-Verlag
In this article we propose a framework for establishing individual structural networks. An individual network is established for each subject using the mean cortical thickness of cortical regions as defined by the AAL atlas. Specifically, for each subject, we compute a similarity matrix of mean cortical thickness between pairs of ...
20
August 2010
Journal of Computational Neuroscience: Volume 29 Issue 1-2, August 2010
Publisher: Springer-Verlag New York, Inc.
State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the ...
Keywords:
Tridiagonal matrix, Neural coding, Hidden Markov model, State-space models
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