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Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第4期20卷 1197-1214页

作者： Qi, Geqi Huang, Ailing Guan, Wei Fan, Lingling Beijing Jiaotong Univ MOE Key Lab Urban Transportat Complex Syst Theory Beijing 100044 Peoples R China

Mobility patterns at region level can provide more macroscopic and intuitive knowledge on how people gather in or depart from the region. However, the analysis and prediction of regional mobility patterns have yet to be effectively addressed. In light of this, using smart card data (SCD) and points of interest (POI) data, a multi-step methodology which integrates the inner-restricted fuzzy C-means clustering, nonnegative tensor factorization and artificial neural network are proposed and implemented in this paper. It overcomes the difficulties in region division, pattern extraction, and prediction. The bus SCD and POI data in Beijing city are utilized for proving the usefulness of the methodology. The regional mobility patterns of bus travellers in Beijing city are extracted from the third-order tensors involving 1110 regions, 34 time slots, and 7 days of the week. The analyzed results show that the proposed methodology has a good performance on predicting the regional mobility patterns based on the regional properties. Furthermore, by considering both of the regional boarding and alighting patterns, the predictions of the regional aggregation pattern can also be achieved. These research achievements can not only provide a deep insight on the human mobility patterns at region level, but also support the evidence-based and forward-looking urban planning and intelligent transportation management.

关键词： Regional mobility pattern smart card data points of Interest inner-restricted fuzzy C-means clustering nonnegative tensor factorization artificial neural network

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ANALYSIS OF ONGOING EEG ELICITED BY NATURAL MUSIC STIMULI USING nonnegative tensor factorization

ANALYSIS OF ONGOING EEG ELICITED BY NATURAL MUSIC STIMULI US...

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European Signal Processing Conference

作者： Fengyu Cong Anh Huy Phan Qibin Zhao Asoke K. Nandi Vinoo Alluri Petri Toiviainen Hanna Poikonen Minna Huotilainen Andrzej Cichocki Tapani Ristaniemi Department of Mathematical Information Technology University of Jyvaskyla Laboratory of Advanced Brain Signal Processing RIKEN Brain Science Institute Department of Electrical Engineering and Electronics The University of Liverpool Finnish Centre of Excellence in Interdisciplinary Music Research University of Jyvaskyla Cognitive Brain Research Unit Institute of Behavioral Sciences University of Helsinki

ISBN: (纸本)9781467310680

This study proposes a six-step approach to analyze ongoing EEG elicited by 512-second long natural music (modern tango). The spectrogram of the ongoing EEG was first produced, and then a fourth-order tensor including the spectrograms of multiple channels of multiple participants was decomposed via nonnegative tensor factorization (NTF) into four factors, including temporal, spectral and spatial components, and multi-domain features of all participants. We found one extracted temporal component by NTF significantly (p ＜ 0.01) correlated with the temporal course of a long-term music feature, 'fluctuation centroid';moreover, the power of posterior alpha activity was found to be associated with this temporal component. Hence, it looks promising to apply the proposed method for analyzing other ongoing EEG elicited by other natural stimuli.

关键词： EEG Ongoing Music nonnegative tensor factorization Spectrogram

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Estimation of a Low-Rank Probability-tensor from Sample Sub-tensors via Joint factorization Minimizing the Kullback-Leibler Divergence 27

Estimation of a Low-Rank Probability-Tensor from Sample Sub-...

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27th European Signal Processing Conference (EUSIPCO)

作者： Yeredor, Arie Haardt, Martin Tel Aviv Univ Sch Elect Engn Tel Aviv Israel Ilmenau Univ Technol Commun Res Lab Ilmenau Germany

ISBN: (纸本)9789082797039

Recently there has been a growing interest in the estimation of the Probability Mass Function (PMF) of discrete random vectors (RVs) from partial observations thereof (namely when observed realizations of the RV are limited to random subsets of its elements). It was shown that under a low-rank assumption on the PMF tensor (and some additional mild conditions), the full tensor can be recovered, e.g., by applying an approximate coupled factorization to empirical estimates of all joint PMFs of subgroups of fixed cardinality larger than two (e.g., triplets). The coupled factorization is based on a Least Squares (LS) fit to the empirically estimated lower-order sub tensors. In this work we take a different approach by trying to fit the coupled factorization to estimated sub-tensors in the sense of minimizing the Kullback-Leibler divergence (KLD) between the estimated and inferred tensors. We explain why the KLD-based fitting is better-suited than LS-based fitting for the problem of PMF estimation, propose an associated minimization approach and demonstrate some advantages over LS-based fitting in this context using simulation results.

关键词： Low-Rank tensor factorization Probability Mass Function (PMF) Approximate Coupled factorization Kullback-Leibler Divergence (KLD) nonnegative tensor factorization Canonical Polyadic Decomposition (CPD)

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DEEP tensor factorization FOR SPATIALLY-AWARE SCENE DECOMPOSITION

DEEP TENSOR FACTORIZATION FOR SPATIALLY-AWARE SCENE DECOMPOS...

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

作者： Casebeer, Jonah Colomb, Michael Smaragdis, Paris Univ Illinois Comp Sci Dept Urbana IL 61801 USA

ISBN: (纸本)9781728111230

We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone. To this end, we formulate a neural network architecture that can be interpreted as a nonnegative tensor factorization of a multi-channel audio recording. By clustering on the learned network parameters corresponding to channel content, we can learn sources' individual spectral dictionaries and their activation patterns over time. Our method allows us to leverage deep learning advances like end-to-end training, while also allowing stochastic minibatch training so that we can feasibly decompose realistic audio scenes that are intractable to decompose using standard methods. This neural network architecture is easily extensible to other kinds of tensor factorizations.

关键词： nonnegative tensor factorization source separation unsupervised learning scene understanding deep learning

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On the equivalence between nonnegative tensor factorization and tensorial probabilistic latent semantic analysis

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APPLIED INTELLIGENCE 2011年第2期35卷 285-295页

作者： Peng, Wei Li, Tao Florida Int Univ Sch Comp Sci Miami FL 33199 USA Xerox Corp Xerox Innovat Grp Rochester NY 14580 USA

Non-negative Matrix factorization (NMF) and Probabilistic Latent Semantic Analysis (PLSA) are two widely used methods for non-negative data decomposition of two-way data (e.g., document-term matrices). Studies have shown that PLSA and NMF (with the Kullback-Leibler divergence objective) are different algorithms optimizing the same objective function. Recently, analyzing multi-way data (i.e., tensors), has attracted a lot of attention as multi-way data have rich intrinsic structures and naturally appear in many real-world applications. In this paper, the relationships between NMF and PLSA extensions on multi-way data, e.g., NTF (Non-negative tensor factorization) and T-PLSA (tensorial Probabilistic Latent Semantic Analysis), are studied. Two types of T-PLSA models are shown to be equivalent to two well-known non-negative factorization models: PARAFAC and Tucker3 (with the KL-divergence objective). NTF and T-PLSA are also compared empirically in terms of objective functions, decomposition results, clustering quality, and computation complexity on both synthetic and real-world datasets. Finally, we show that a hybrid method by running NTF and T-PLSA alternatively can successfully jump out of each other's local minima and thus be able to achieve better clustering performance.

关键词： nonnegative tensor factorization Probabilistic latent semantic analysis Clustering

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MULTICHANNEL nonnegative tensor factorization WITH STRUCTURED CONSTRAINTS FOR USER-GUIDED AUDIO SOURCE SEPARATION

MULTICHANNEL NONNEGATIVE TENSOR FACTORIZATION WITH STRUCTURE...

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

作者： Ozerov, Alexey Fevotte, Cedric Blouet, Raphael Durrieu, Jean-Louis INRIA Campus Beaulieu F-35042 Rennes France

ISBN: (纸本)9781457705397

Separating multiple tracks from professionally produced music recordings (PPMRs) is still a challenging problem. We address this task with a user-guided approach in which the separation system is provided segmental information indicating the time activations of the particular instruments to separate. This information may typically be retrieved from manual annotation. We use a so-called multichannel nonnegative tensor factorization (NTF) model, in which the original sources are observed through a multichannel convolutive mixture and in which the source power spectrograms are jointly modeled by a 3-valence (time/frequency/source) tensor. Our user-guided separation method produced competitive results at the 2010 Signal Separation Evaluation Campaign, with sufficient quality for real-world music editing applications.

关键词： Audio source separation user-guided nonnegative tensor factorization generalized expectation maximization

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

作者： Yoshii, Kazuyoshi Kyoto Univ Grad Sch Informat Kyoto Japan RIKEN Ctr Adv Intelligence Project AIP Tokyo Japan

ISBN: (纸本)9781538646588

This paper presents an ultimate extension of nonnegative matrix factorization (NMF) for audio source separation based on full covariance modeling over all the time-frequency (TF) bins of the complex spectrogram of an observed mixture signal. Although NMF has been widely used for decomposing an observed power spectrogram in a TF-wise manner, it has a critical limitation that the phase values of interdependent TF bins cannot be dealt with. This problem has been solved only partially by several phase-aware extensions of NMF that decompose an observed complex spectrogram in an time- and/or frequency-wise manner. In this paper, we propose correlated tensor factorization (CTF) that approximates the full covariance matrix over all TF bins as the sum of the Kronecker products between basis covariance matrices over frequency bands and the corresponding ones over time frames. All the TF bins of the complex spectrogram of each source signal are estimated jointly in an interdependent manner via Wiener filtering. We discuss how to reduce the computational cost of CTF and report the results of comparative evaluation of CTF with its special cases such as NMF and positive semidefinite tensor factorization (PSDTF).

关键词： Source separation nonnegative matrix factorization nonnegative tensor factorization positive semidefinite tensor factorization correlated tensor factorization

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COLLABORATIVE SPEECH DEREVERBERATION: REGULARIZED tensor factorization FOR CROWDSOURCED MULTI-CHANNEL RECORDINGS 26

COLLABORATIVE SPEECH DEREVERBERATION: REGULARIZED TENSOR FAC...

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European Signal Processing Conference (EUSIPCO)

作者： Wager, Sanna Kim, Minje Indiana Univ Sch Informat Comp & Engn Bloomington IN 47408 USA

ISBN: (纸本)9789082797015

We propose a regularized nonnegative tensor factorization (NTF) model for multi-channel speech dereverberation that incorporates prior knowledge about clean speech. The approach models the problem as recovering a signal convolved with different room impulse responses, allowing the dereverberation problem to benefit from microphone arrays. The factorization learns both individual reverberation filters and channel-specific delays, which makes it possible to employ an ad-hoc microphone array with heterogeneous sensors (such as multi-channel recordings by a crowd) even if they are not synchronized. We integrate two prior-knowledge regularization schemes to increase the stability of dereverberation performance. First, a nonnegative Matrix factorization (NMF) inner routine is introduced to inform the original NTF problem of the pre-trained clean speech basis vectors, so that the optimization process can focus on estimating their activations rather than the whole clean speech spectra. Second, the NMF activation matrix is further regularized to take on characteristics of dry signals using sparsity and smoothness constraints. Empirical dereverberation results on different simulated reverberation setups show that the prior-knowledge regularization schemes improve both recovered sound quality and speech intelligibility compared to a baseline NTF approach.

关键词： multi-channel dereverberation nonnegative matrix factorization nonnegative tensor factorization collaborative audio enhancement speech enhancement

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Using high-resolution binary layers and a low-resolution multibit backlight for a layered light-field display

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OPTICAL ENGINEERING 2018年第6期57卷 061607-061607页

作者： Kobayashi, Yuto Takahashi, Keita Fujii, Toshiaki Nagoya Univ Dept Informat & Commun Engn Nagoya Aichi Japan

We propose a structure for a layered light-field display composed of high-resolution binary layers and a low-resolution, multibit backlight. This structure aims to increase the upper bound of the spatial frequency while also reducing the total number of bits for the display. The increased layer resolution increases the upper bound of the spatial frequency, meaning that the display can reproduce an object with a large amount of pop-out more clearly than can a conventional light-field display. In contrast, limiting the layers' transmittance to binary (on/off) levels reduces the total number of bits for the display, thus maintaining the high efficiency of light-field representation. The low-resolution backlight, whose pixels can take multibit values, compensates the number of intensity levels, which would otherwise be quite limited with only the binary layers. Through analytical and experimental results, we show that a display based on the proposed structure can reproduce a light field with high quality and high efficiency as a result of combining the high-resolution binary layers and low-resolution backlight. (C) The Authors.

关键词： light-field display compressive display nonnegative tensor factorization

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kazuyoshi Yoshii Graduate School of Informatics Kyoto University Japan RIKEN Center for Advanced Intelligence Project (AIP) Japan

ISBN: (纸本)9781538646595

This paper presents an ultimate extension of nonnegative matrix factorization (NMF) for audio source separation based on full covariance modeling over all the time-frequency (TF) bins of the complex spectrogram of an observed mixture signal. Although NMF has been widely used for decomposing an observed power spectrogram in a TF-wise manner, it has a critical limitation that the phase values of interdependent TF bins cannot be dealt with. This problem has been solved only partially by several phase-aware extensions of NMF that decompose an observed complex spectrogram in an time-and/or frequency-wise manner. In this paper, we propose correlated tensor factorization (CTF) that approximates the full covariance matrix over all TF bins as the sum of the Kronecker products between basis covariance matrices over frequency bands and the corresponding ones over time frames. All the TF bins of the complex spectrogram of each source signal are estimated jointly in an interdependent manner via Wiener filtering. We discuss how to reduce the computational cost of CTF and report the results of comparative evaluation of CTF with its special cases such as NMF and positive semidefinite tensor factorization (PSDTF).

关键词： Source separation nonnegative matrix factorization nonnegative tensor factorization positive semidefinite tensor factorization correlated tensor factorization Source separation NMF protocol NFIC gene matrix decomposition Spectrogram factorization Audio tensor Bins variance covariance matrix orthogonality Wiener filters

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