Search Results - "Skau, Erik"

Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC by Alexandrov, Boian, DeSantis, Derek F., Manzini, Gianmarco, Skau, Erik W.

“…There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large…”
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Distributed non-negative matrix factorization with determination of the number of latent features by Chennupati, Gopinath, Vangara, Raviteja, Skau, Erik, Djidjev, Hristo, Alexandrov, Boian

“…The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for…”
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An Out of Memory tSVD for Big-Data Factorization by Carrillo-Cabada, Hector, Skau, Erik, Chennupati, Gopinath, Alexandrov, Boian, Djidjev, Hristo

“…Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and…”
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Automatic Model Determination for Quaternion NMF by Sanchez, Giancarlo, Skau, Erik, Alexandrov, Boian

“…Nonnegative Matrix Factorization (NMF) is a well-known method for Blind Source Separation (BSS). Recently, BSS for polarized signals in spectropolarimetric…”
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Challenging the Curse of Dimensionality in Multidimensional Numerical Integration by Using a Low-Rank Tensor-Train Format by Alexandrov, Boian, Manzini, Gianmarco, Skau, Erik W., Truong, Phan Minh Duc, Vuchov, Radoslav G.

“…Numerical integration is a basic step in the implementation of more complex numerical algorithms suitable, for example, to solve ordinary and partial…”
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Finding the Number of Latent Topics with Semantic Non-negative Matrix Factorization by Vangara, Raviteja, Bhattarai, Manish, Skau, Erik, Chennupati, Gopinath, Djidjev, Hristo, Tierney, Thomas, Smith, James P., Stanev, Valentin G., Alexandrov, Boian S.

“…Topic modeling, or identifying the set of topics that occur in a collection of articles, is one of the primary objectives of text mining. Typically, a text…”
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Boolean Matrix Factorization via Nonnegative Auxiliary Optimization by Truong, Duc P., Skau, Erik, Desantis, Derek, Alexandrov, Boian

“…A novel approach to Boolean matrix factorization (BMF) is presented. Instead of solving the BMF problem directly, this approach solves a nonnegative…”
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Determination of latent dimensionality in international trade flow by Truong, Duc P, Skau, Erik, Valtchinov, Vladimir I, Alexandrov, Boian S

“…Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such…”
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Fusing Heterogeneous Data: A Case for Remote Sensing and Social Media by Wang, Han, Skau, Erik, Krim, Hamid, Cervone, Guido

“…Data heterogeneity can pose a great challenge to process and systematically fuse low-level data from different modalities with no recourse to heuristics and…”
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Distributed non-negative RESCAL with automatic model selection for exascale data by Bhattarai, Manish, kharat, Namita, Boureima, Ismael, Skau, Erik, Nebgen, Benjamin, Djidjev, Hristo, Rajopadhye, Sanjay, Smith, James P., Alexandrov, Boian

“…With the boom in the development of computer hardware and software, social media, IoT platforms, and communications, there has been exponential growth in the…”
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Distributed out-of-memory NMF on CPU/GPU architectures by Boureima, Ismael, Bhattarai, Manish, Eren, Maksim, Skau, Erik, Romero, Philip, Eidenbenz, Stephan, Alexandrov, Boian

“…We propose an efficient distributed out-of-memory implementation of the non-negative matrix factorization (NMF) algorithm for heterogeneous…”
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Distributed out-of-memory NMF on CPU/GPU architectures by Boureima, Ismael, Bhattarai, Manish, Eren, Maksim, Skau, Erik, Romero, Philip, Eidenbenz, Stephan, Alexandrov, Boian

“…We propose an efficient distributed out-of-memory implementation of the non-negative matrix factorization (NMF) algorithm for heterogeneous…”
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Correction to: Distributed out-of-memory NMF on CPU/GPU architectures by Boureima, Ismael, Bhattarai, Manish, Eren, Maksim, Skau, Erik, Romero, Philip, Eidenbenz, Stephan, Alexandrov, Boian

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Challenging the Curse of Dimensionality in Multidimensional Numerical Integration by Using a Low-Rank Tensor-Train Format by Alexandrov, Boian, Manzini, Gianmarco, Skau, Erik W., Truong, Phan Duc, Vuchov, Radoslav G.

“…Numerical integration is a basic step in the implementation of more complex numerical algorithms suitable, for example, to solve ordinary and partial…”
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An Out of Memory tSVD for Big-Data Factorization by Carrillo-Cabada, Hector, Skau, Erik, Chennupati, Gopinath, Alexandrov, Boian, Djidjev, Hristo

“…Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and…”
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Image classification: A hierarchical dictionary learning approach by Mahdizadehaghdam, Shahin, Liyi Dai, Krim, Hamid, Skau, Erik, Han Wang

“…Hierarchical dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method…”
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Pansharpening via coupled triple factorization dictionary learning by Skau, Erik, Wohlberg, Brendt, Krim, Hamid, Liyi Dai

“…Data fusion is the operation of integrating data from different modalities to construct a single consistent representation. This paper proposes variations of…”
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Relaxations to Sparse Optimization Problems and Applications by Skau, Erik West

“…Parsimony is a fundamental property that is applied to many characteristics in a variety of fields. Of particular interest are optimization problems that apply…”
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Non-parametric bounds on the nearest neighbor classification accuracy based on the Henze-Penrose metric by Ghanem, Sally, Skau, Erik, Krim, Hamid, Clouse, Hamilton Scott, Sakla, Wesam

“…Analysis procedures for higher-dimensional data are generally computationally costly; thereby justifying the high research interest in the area. Entropy-based…”
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A Fast Parallel Algorithm for Convolutional Sparse Coding by Skau, Erik, Wohlberg, Brendt

“…The current leading algorithms for convolutional sparse coding are not inherently parallelizable, and therefore are not able to fully exploit modern multi-core…”
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