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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Bilen, Cagdas Ozerov, Alexey Perez, Patrick Technicolor 975 Ave Champs BlancsCS 17616 F-35576 Cesson Sevigne France

ISBN: (纸本)9781479974504

The paradigm of using a very simple encoder and a sophisticated decoder for compression of signals became popular with the theory of distributed coding and it has been exercised for the compression of various types of signals such as images and video. The theory of compressive sampling later introduced a similar concept but with the focus on guarantees of signal recovery using sparse and low rank priors lying in an incoherent domain to the domain of sampling. In this paper, we bring together the concepts introduced in distributed coding and compressive sampling with the informed source separation, in which the goal is to efficiently compress the audio sources so that they can be decoded with the knowledge of the mixture of the sources. The proposed framework uses a very simple time domain sampling scheme to encode the sources, and a sophisticated decoding algorithm that makes use of the low rank non-negative tensor factorization model of the distribution of short-time Fourier transform coefficients to recover the sources, which is a direct application of the principles of both compressive sampling and distributed coding.

关键词： Informed source separation low complexity encoder compressive sampling nonnegative tensor factorization generalized expectation-maximization

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A Parallel nonnegative tensor factorization Algorithm for Mining Global Climate Data

A Parallel Nonnegative Tensor Factorization Algorithm for Mi...

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9th International Conference on Computational Science

作者： Zhang, Qiang Berry, Michael W. Lamb, Brain T. Samuel, Tabitha Wake Forest Univ Hlth Sci Dept Biostat Sci Med Ctr Blvd Winston Salem NC 27157 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9783642019722

Increasingly large datasets acquired by NASA for global climate studies demand larger computation memory and higher CPU speed to mine out useful and revealing information. While boosting the CPU frequency is getting harder, clustering multiple lower performance computers thus becomes increasingly popular. This prompts a trend of parallelizing the existing algorithms kind methods by mathematicians and computer scientists. In this paper, we take oil the task of parallelizing the nonnegative tensor factorization (NTF) method, with the purposes of distributing large datasets into each cluster node and thus reducing the demand on a single node, blocking and localizing the computation at the maximal degree, and finally minimizing the memory use for storing matrices or tensors by exploiting their structural relationships. Numerical experiments were performed oil a NASA global sea surface temperature dataset and result factors were analyzed and discussed.

关键词： nonnegative tensor factorization parallel computation data mining global climate

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Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework

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JOURNAL OF GLOBAL OPTIMIZATION 2014年第2期58卷 285-319页

作者： Kim, Jingu He, Yunlong Park, Haesun Nokia Inc Sunnyvale CA USA Georgia Inst Technol Sch Math Atlanta GA 30332 USA Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

We review algorithms developed for nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) from a unified view based on the block coordinate descent (BCD) framework. NMF and NTF are low-rank approximation methods for matrices and tensors in which the low-rank factors are constrained to have only nonnegative elements. The nonnegativity constraints have been shown to enable natural interpretations and allow better solutions in numerous applications including text analysis, computer vision, and bioinformatics. However, the computation of NMF and NTF remains challenging and expensive due the constraints. Numerous algorithmic approaches have been proposed to efficiently compute NMF and NTF. The BCD framework in constrained non-linear optimization readily explains the theoretical convergence properties of several efficient NMF and NTF algorithms, which are consistent with experimental observations reported in literature. In addition, we discuss algorithms that do not fit in the BCD framework contrasting them from those based on the BCD framework. With insights acquired from the unified perspective, we also propose efficient algorithms for updating NMF when there is a small change in the reduced dimension or in the data. The effectiveness of the proposed updating algorithms are validated experimentally with synthetic and real-world data sets.

关键词： nonnegative matrix factorization nonnegative tensor factorization Low-rank approximation Block coordinate descent

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Line search and trust region strategies for canonical decomposition of semi-nonnegative semi-symmetric 3rd order tensors

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LINEAR ALGEBRA AND ITS APPLICATIONS 2014年 450卷 334-374页

作者： Coloigner, Julie Karfoul, Ahmad Albera, Laurent Comon, Pierre INSERM UMR 1099 F-85000 Rennes France Univ Rennes 1 LTSI Lab F-35000 Rennes France Inst Natl Rech Informat & Automat Ctr Inria Rennes Bretagne Atlantique F-35042 Rennes France AL Baath Univ Fac Mech & Elect Engn Homs Syria Ecole Natl Super Electrochim & Electrome Grenoble Thermodynam & Physicochim Met Lab CNRS Lab GIPSAUMR 5216 F-38402 St Martin Dheres France

Numerical solutions are proposed to fit the CanDecomp/ParaFac (CP) model of real three-way arrays, when the latter are both nonnegative and symmetric in two modes. In other words, a semi-nonnegative INDSCAL analysis is performed. The nonnegativity constraint is circumvented by means of changes of variable into squares, leading to an unconstrained problem. In addition, two globalization strategies are studied, namely line search and trust region. Regarding the former, a global plane search scheme is considered. It consists in computing, for a given direction, one or two optimal stepsizes, depending on whether the same stepsize is used in various updating rules. Moreover, we provide a compact matrix form for the derivatives of the objective function. This allows for a direct implementation of several iterative algorithms such as conjugate gradient, Levenberg-Marquardt and Newton-like methods, in matrix programming environments like MATLAB. Our numerical results show the advantage of our optimization strategies when combined with a priori information such as partial symmetry. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Canonical polyadic model nonnegative matrix factorization nonnegative tensor factorization Semi-nonnegative INDSCAL analysis CanDecomp ParaFac Nonlinear optimization Matrix calculus

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Limestone: High-throughput candidate phenotype generation via tensor factorization

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JOURNAL OF BIOMEDICAL INFORMATICS 2014年 52卷 199-211页

作者： Ho, Joyce C. Ghosh, Joydeep Steinhubl, Steve R. Stewart, Walter F. Denny, Joshua C. Malin, Bradley A. Sun, Jimeng Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Scripps Hlth Scripps Translat Sci Inst La Jolla CA 92037 USA Sutter Hlth Sutter Hlth Res Dev & Disseminat Team Walnut Creek CA 94598 USA Vanderbilt Univ Dept Biomed Informat Nashville TN 37232 USA Vanderbilt Univ Dept Med Nashville TN 37232 USA Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37232 USA Georgia Inst Technol Coll Comp Sch Computat Sci & Engn Atlanta GA 30332 USA

The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately. EHR data do not always directly and reliably map to medical concepts that clinical researchers need or use. Some recent studies have focused on EHR-derived phenotyping, which aims at mapping the EHR data to specific medical concepts;however, most of these approaches require labor intensive supervision from experienced clinical professionals. Furthermore, existing approaches are often disease-centric and specialized to the idiosyncrasies of the information technology and/or business practices of a single healthcare organization. In this paper, we propose Limestone, a nonnegative tensor factorization method to derive phenotype candidates with virtually no human supervision. Limestone represents the data source interactions naturally using tensors (a generalization of matrices). In particular, we investigate the interaction of diagnoses and medications among patients. The resulting tensor factors are reported as phenotype candidates that automatically reveal patient clusters on specific diagnoses and medications. Using the proposed method, multiple phenotypes can be identified simultaneously from data. We demonstrate the capability of Limestone on a cohort of 31,815 patient records from the Geisinger Health System. The dataset spans 7 years of longitudinal patient records and was initially constructed for a heart failure onset prediction study. Our experiments demonstrate the robustness, stability, and the conciseness of Limestone-derived phenotypes. Our results show that using only 40 phenotypes, we can outperform the original 640 features (169 diagnosis categories and 471 medication types) to achieve an area under the receiver operator characteristic curve (AUC) of 0.720 (95% CI 0.715 to 0.725). Moreover, in c

关键词： Dimensionality reduction nonnegative tensor factorization EHR phenotyping

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DETERMINING SEMANTIC VALENCES OF ONTOLOGY CONCEPTS BY MEANS OF nonnegative factorization OF tensorS OF LARGE TEXT CORPORA

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CYBERNETICS AND SYSTEMS ANALYSIS 2014年第3期50卷 327-337页

作者： Anisimov, A. V. Marchenko, O. O. Vozniuk, T. G. Taras Shevchenko Natl Univ Kyiv Kiev Ukraine

This paper describes a method for automatic detection of semantic relations between concept nodes of a networked ontological knowledge base by analyzing matrices of semantic- syntactic valences of words. These matrices are obtained by means of nonnegative factorization of tensors of syntactic compatibility of words. Such tensors are generated in the course of frequency analysis of syntactic structures of sentences taken from large text corpora of English Wikipedia and Simple English Wikipedia entries.

关键词： automatic knowledge acquisition corpus linguistics ontology nonnegative tensor factorization

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Focus 3D: Compressive Accommodation Display

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ACM TRANSACTIONS ON GRAPHICS 2013年第5期32卷 1–13页

作者： Maimone, Andrew Wetzstein, Gordon Hirsch, Matthew Lanman, Douglas Raskar, Ramesh Fuchs, Henry Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA MIT Media Lab Cambridge MA 02139 USA

We present a glasses-free 3D display design with the potential to provide viewers with nearly correct accommodative depth cues, as well as motion parallax and binocular cues. Building on multilayer attenuator and directional backlight architectures, the proposed design achieves the high angular resolution needed for accommodation by placing spatial light modulators about a large lens: one conjugate to the viewer's eye, and one or more near the plane of the lens. nonnegative tensor factorization is used to compress a high angular resolution light field into a set of masks that can be displayed on a pair of commodity LCD panels. By constraining the tensor factorization to preserve only those light rays seen by the viewer, we effectively steer narrow high-resolution viewing cones into the user's eyes, allowing binocular disparity, motion parallax, and the potential for nearly correct accommodation over a wide field of view. We verify the design experimentally by focusing a camera at different depths about a prototype display, establish formal upper bounds on the design's accommodation range and diffraction-limited performance, and discuss practical limitations that must be overcome to allow the device to be used with human observers.

关键词： Algorithms Design Experimentation Human Factors Eye accommodation retinal blur compressive displays automultiscopic displays light fields high angular resolution backlighting nonnegative tensor factorization

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SOUND SOURCE SEPARATION BASED ON NON-NEGATIVE tensor factorization INCORPORATING SPATIAL CUE AS PRIOR KNOWLEDGE

SOUND SOURCE SEPARATION BASED ON NON-NEGATIVE TENSOR FACTORI...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mitsufuji, Yuki Roebel, Axel Sony Corp Tokyo Japan IRCAM CNRS UPMC UMR 9912 F-75004 Paris France

ISBN: (纸本)9781479903566

This paper concerns a new method of source separation that uses a spatial cue given by a user or from accompanying images to extract a target sound. The algorithm is based on nonnegative tensor factorization (NTF), which decomposes multichannel spectrograms into three matrices. The components of one of the three matrices represent spatial information and are associated with the spatial cue, thus indicating which bins of the spectrogram should be given preference. When a spatial cue is available, this method has a great advantage over conventional PARAFAC-NTF in terms of both computational costs and separation quality, as measured by evaluation metrics such as SDR, SIR and SAR.

关键词： Audio source separation Signal reconstruction Sparse representation nonnegative tensor factorization

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Seeking an appropriate alternative least squares algorithm for nonnegative tensor factorizations

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NEURAL COMPUTING & APPLICATIONS 2012年第4期21卷 623-637页

作者： Anh Huy Phan Cichocki, Andrzej RIKEN BSI Lab Adv Brain Signal Proc Wako Saitama Japan

Alternative least squares (ALS) algorithm is considered as a "work-horse" algorithm for general tensor factorizations. A common form of this algorithm for nonnegative tensor factorizations (NTF) is always combined with a nonlinear projection (rectifier) to enforce nonnegative entries during the estimation. Such simple modification often provides acceptable results for general data. However, this does not establish an appropriate ALS algorithm for NTF. This kind of ALS algorithm often converges slowly, or cannot converge to the desired solution, especially for collinear data. To this end, in this paper, we reinvestigate the nonnegative quadratic programming, propose a recursive method for solving this problem. Then, we formulate a novel ALS algorithm for NTF. The validity and high performance of the proposed algorithm has been confirmed for difficult benchmarks, and also in an application of object classification, and analysis of EEG signals.

关键词： Canonical polyadic decomposition (CP) PARAFAC nonnegative tensor factorization NMF nonnegative quadratic programming Parallel computing ALS Object classification Clustering EEG analysis

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nonnegative matrix and tensor factorization

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IEEE SIGNAL PROCESSING MAGAZINE 2008年第1期25卷 142-145页

作者： Cichocki, Andrzej Zdunek, Rafal Amari, Shun-Ichi RIKEN BSI Lab Adv Brain Signal Proc Wako Saitama Japan Wroclaw Univ Technol PL-50370 Wroclaw Poland

In these lecture notes, the authors have outlined several approaches to solve a NMF/NTF problem. The following main conclusions can be drawn: 1) Multiplicative algorithms are not necessary the best approaches for NMF, especially if data representations are not very redundant or sparse. 2) Much better performance can be achieved using the FP-ALS (especially for large-scale problems), IPC, and QN methods. 3) To achieve high performance it is quite important to use the multilayer structure with multistart initialization conditions. 4) To estimate physically meaningful nonnegative components it is often necessary to use some a priori knowledge and impose additional constraints or regularization terms (to control sparsity, boundness, continuity or smoothness of the estimated nonnegative components).

关键词： matrix decomposition signal processing tensors FP-ALS IPC methods QN methods large-scale problems multilayer structure multistart initialization conditions nonnegative components nonnegative matrix factorization nonnegative tensor factorization Representation of data nonnegative tensor signal processing matrix decomposition multilayer structures redundant constraint

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