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Anchor-Free Correlated Topic Modeling

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019年第5期41卷 1056-1071页

作者： Fu, Xiao Huang, Kejun Sidiropoulos, Nicholas D. Shi, Qingjiang Hong, Mingyi Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China

In topic modeling, identifiability of the topics is an essential issue. Many topic modeling approaches have been developed under the premise that each topic has a characteristic anchor word that only appears in that topic. The anchor-word assumption is fragile in practice, because words and terms have multiple uses;yet it is commonly adopted because it enables identifiability guarantees. Remedies in the literature include using three- or higher-order word co-occurence statistics to come up with tensor factorization models, but such statistics need many more samples to obtain reliable estimates, and identifiability still hinges on additional assumptions, such as consecutive words being persistently drawn from the same topic. In this work, we propose a new topic identification criterion using second order statistics of the words. The criterion is theoretically guaranteed to identify the underlying topics even when the anchor-word assumption is grossly violated. An algorithm based on alternating optimization, and an efficient primal-dual algorithm are proposed to handle the resulting identification problem. The former exhibits high performance and is completely parameter-free;the latter affords up to 200 times speedup relative to the former, but requires step-size tuning and a slight sacrifice in accuracy. A variety of real text copora are employed to showcase the effectiveness of the approach, where the proposed anchor-free method demonstrates substantial improvements compared to a number of anchor-word based approaches under various evaluation metrics.

关键词： Topic modeling identifiability anchor free sufficiently scattered non-convex optimization non negative matrix factorization

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Divergence Functions of non negative matrix factorization: A Comparison Study

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2011年第10期40卷 1594-1612页

作者： Zhang, Zhong-Yuan Cent Univ Finance & Econ Sch Stat Beijing Peoples R China

non negative matrix factorization (NMF) has become one of the most popular models in data mining for its good performance in unsupervised learning applications. Recently, a variety of divergence functions have been extensively studied for NMF. But, there is still lack of analysis on the relationships between the divergence functions and the applications. This article tries to give some preliminary results on this interesting problem. Our experiments show that the most familiar two divergence functions-the least squares error and the K-L divergence-are competent for unsupervised learning such as gene expression data clustering and image processing.

关键词： Blind source separation Comparison Clustering Divergence function Image processing non negative matrix factorization

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A Complex matrix factorization Approach to Joint Modeling of Magnitude and Phase for Source Separation 19

A Complex Matrix Factorization Approach to Joint Modeling of...

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19th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Ahuja, Chaitanya Nathwani, Karan Hegde, Rajesh M. Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Indian Inst Technol Jammu Dept Elect Engn Jammu India Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India

ISBN: (纸本)9781728153414

Conventional NMF methods for source separation factorize the matrix of spectral magnitudes. Spectral Phase is not included in the decomposition process of these methods. However, phase of the speech mixture is generally used in reconstructing the target speech signal. This results in undesired traces of interfering sources in the target signal. In this paper the spectral phase is incorporated in the decomposition process itself. Additionally, the complex matrix factorization problem is reduced to an NMF problem using simple transformations. This results in effective separation of speech mixtures since both magnitude and phase are utilized jointly in the separation process. Improvement in source separation results are demonstrated using objective quality evaluations on the GRID corpus. As a side result, we have also investigated the intelligibility improvement aspect of target speaker in the presence of interfering speaker.

关键词： non negative matrix factorization Complex matrix factorization Source Separation Phase Reconstruction

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Network-Word Embedding for Dynamic Text Attributed Networks 12

Network-Word Embedding for Dynamic Text Attributed Networks

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12th IEEE International Conference on Semantic Computing (ICSC)

作者： Ito, Hiroyoshi Komamizu, Takahiro Amagasa, Toshiyuki Kitagawa, Hiroyuki Univ Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki Japan

ISBN: (纸本)9781538644072

Network embedding enables to apply off-the-shelf machine learning methods to the nodes on the network. Leveraging the textual information associated with nodes into network embedding methods is advantageous. However, only a few works try to leverage textual information to network embeddings. Moreover, the structure of networks and associated texts could be dynamically changed over time, this property leads shifts of the vector representation of nodes and words. However, to the best of our knowledge, none of previous network embedding methods considers chronological changes of vector representations of nodes and words. In this paper, we propose a dynamic text attribute network embedding method, which embeds the nodes and the words in a cooperative manner and takes chronological changes of vector representations of nodes and words into account. Experimental results show that (1) vector representations of nodes of our method achieve higher accuracy than baseline methods in classification and clustering tasks;(2) The vector representations of nodes and words successfully capture semantic similarity;And (3) our method successfully capture the chronological change of the vector representations over time.

关键词： dynamic text attributed network network embedding non negative matrix factorization word embedding

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Data Clustering Based on Label Consistent Constraint matrix factorization 10

Data Clustering Based on Label Consistent Constraint Matrix ...

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10th International Conference on Digital Image Processing (ICDIP)

作者： Long, Xianzhong Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol 9 Wenyuan Rd Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc 9 Wenyuan Rd Nanjing Jiangsu Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

The schemes based on matrix factorization have been applied in many fields, such as recommender system, image classification, data clustering, face recognition and so on. Among them, non negative matrix factorization (NMF) and concept factorization (CF) are the two most commonly used matrix decomposition techniques. NMF decomposes a matrix into the product of two non negative matrices and CF divides a matrix into the product of three matrices. CF is considered as one variant of NMF. The biggest difference between them is that CF can be executed in a kernel space. The development of supervised learning methods show that label information is critical to enhance the model's ability. In this paper, NMF and CF based on label consistent constraint methods are presented respectively for data clustering. The corresponding multiplicative update solutions, parameters selection and convergence verification are given. Clustering results on three data sets reveal that our methods outperform the state-of-the-art algorithms in terms of accuracy and normalized mutual information.

关键词： non negative matrix factorization Concept factorization Label Consistent Constraint Data Clustering

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Extractive multi-document summarization using population-based multicriteria optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 86卷 385-397页

作者： John, Ansamma Premjith, P. S. Wilscy, M. Univ Kerala Dept Comp Sci Kariyavattom Kerala India TKM Coll Engn Dept Comp Sci Kollam Kerala India

Multi-document summarization is the process of extracting salient information from a set of source texts and present that information to the user in a condensed form. In this paper, we propose a multi document summarization system which generates an extractive generic summary with maximum relevance and minimum redundancy by representing each sentence of the input document as a vector of words in Proper Noun, Noun, Verb and Adjective set. Five features, such as TF_ISF, Aggregate Cross Sentence Similarity, Title Similarity, Proper Noun and Sentence Length associated with the sentences, are extracted, and scores are assigned to sentences based on these features. Weights that can be assigned to different features may vary depending upon the nature of the document, and it is hard to discover the most appropriate weight for each feature, and this makes generation of a good summary a very tough task without human intelligence. Multi-document summarization problem is having large number of decision parameters and number of possible solutions from which most optimal summary is to be generated. Summary generated may not guarantee the essential quality and may be far from the ideal human generated summary. To address this issue, we propose a population-based multicriteria optimization method with multiple objective functions. Three objective functions are selected to determine an optimal summary, with maximum relevance, diversity, and novelty, from a global population of summaries by considering both the statistical and semantic aspects of the documents. Semantic aspects are considered by Latent Semantic Analysis (LSA) and non negative matrix factorization (NMF) techniques. Experiments have been performed on DUC 2002, DUC 2004 and DUC 2006 datasets using ROUGE tool kit. Experimental results show that our system outperforms the state of the art works in terms of Recall and Precision. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-document summarization Multicriteria optimization Latent semantic analysis non negative matrix factorization DUC ROUGE

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Multivariate unmixing approaches on Raman images of plant cell walls: new insights or overinterpretation of results?

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PLANT METHODS 2018年第1 p52-52期14卷 52-52页

作者： Prats-Mateu, Batirtze Felhofer, Martin de Juan, Anna Gierlinger, Notburga BOKU Univ Nat Resources & Life Sci Dept Nanobiotechnol Muthgasse 11-2 A-1190 Vienna Austria Univ Barcelona Chemometr Grp Diagonal 645 E-08028 Barcelona Spain Eidgenoss Tech Hsch Zurich Honggerberg Inst Bldg Mat CH-8093 Zurich Switzerland Empa Swiss Fed Labs Mat Testing & Res Appl Wood Res Lab Uberland Str 129 CH-8600 Dubendorf Switzerland

Background: Plant cell walls are nanocomposites based on cellulose microfibrils embedded in a matrix of polysaccharides and aromatic polymers. They are optimized for different functions (e.g. mechanical stability) by changing cell form, cell wall thickness and composition. To reveal the composition of plant tissues in a non-destructive way on the microscale, Raman imaging has become an important tool. Thousands of Raman spectra are acquired, each one being a spatially resolved molecular fingerprint of the plant cell wall. Nevertheless, due to the multicomponent nature of plant cell walls, many bands are overlapping and classical band integration approaches often not suitable for imaging. Multivariate data analysing approaches have a high potential as the whole wavenumber region of all thousands of spectra is analysed at once. Results: Three multivariate unmixing algorithms, vertex component analysis, non-negative matrix factorization and multivariate curve resolution-alternating least squares were applied to find the purest components within datasets acquired from micro-sections of spruce wood and Arabidopsis. With all three approaches different cell wall layers (including tiny S1 and S3 with 0.09-0.14 mu m thickness) and cell contents were distinguished and endmember spectra with a good signal to noise ratio extracted. Baseline correction influences the results obtained in all methods as well as the way in which algorithm extracts components, i.e. prioritizing the extraction of positive endmembers by sequential orthogonal projections in VCA or performing a simultaneous extraction of non-negative components aiming at explaining the maximum variance in NMF and MCR-ALS. Other constraints applied (e.g. closure in VCA) or a previous principal component analysis filtering step in MCR-ALS also contribute to the differences obtained. Conclusions: VCA is recommended as a good preliminary approach, since it is fast, does not require setting many input parameters and the endm

关键词： Confocal Raman microscopy hyperspectral imaging vertex component analysis non negative matrix factorization multivariate curve resolution plant cell wall wood Arabidopsis

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Coupling spectral analysis and hidden Markov models for the segmentation of behavioural patterns

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MOVEMENT ECOLOGY 2017年第1 p20-20期5卷 20页

作者： Heerah, Karine Woillez, Mathieu Fablet, Ronan Garren, Francois Martin, Stephane De Pontual, Helene IFREMER Sci & Technol Halieut F-29280 Plouzane CS France IMT Atlantique Univ Bretagne Loire F-29238 Brest France

Background: Movement pattern variations are reflective of behavioural switches, likely associated with different life history traits in response to the animals' abiotic and biotic environment. Detecting these can provide rich information on the underlying processes driving animal movement patterns. However, extracting these signals from movement time series, requires tools that objectively extract, describe and quantify these behaviours. The inference of behavioural modes from movement patterns has been mainly addressed through hidden Markov models. Until now, the metrics implemented in these models did not allow to characterize cyclic patterns directly from the raw time series. To address these challenges, we developed an approach to i) extract new metrics of cyclic behaviours and activity levels from a time-frequency analysis of movement time series, ii) implement the spectral signatures of these cyclic patterns and activity levels into a HMM framework to identify and classify latent behavioural states. Results: To illustrate our approach, we applied it to 40 high-resolution European sea bass depth time series. Our results showed that the fish had different activity regimes, which were also associated (or not) with the spectral signature of different environmental cycles. Tidal rhythms were observed when animals tended to be less active and dived shallower. Conversely, animals exhibited a diurnal behaviour when more active and deeper in the water column. The different behaviours were well defined and occurred at similar periods throughout the annual cycle amongst individuals, suggesting these behaviours are likely related to seasonal functional behaviours (e.g. feeding, migrating and spawning). Conclusions: The innovative aspects of our method lie within the combined use of powerful, but generic, mathematical tools (spectral analysis and hidden Markov Models) to extract complex behaviours from 1-D movement time series. It is fully automated which makes it suitable

关键词： Fourier transform non negative matrix factorization Classification Animal behaviour European sea bass Movement ecology Diurnal and tidal cycles Biologging Data storage tags

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NMF-based Label Space factorization for Multi-label Classification 16

NMF-based Label Space Factorization for Multi-label Classifi...

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16th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Firouzi, Mohammad Karimian, Mahmood Baghshah, Mahdieh Soleymani Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781538614174

Multi-label classification is a learning task in which each data sample can belong to more than one class. Until now, some methods that are based on reducing the dimensionality of the label space have been proposed. However, these methods have not used specific properties of the label space for this purpose. In this paper, we intend to find a hidden space in which both the input feature vectors and the label vectors are embedded. We propose a modified non-negative matrix factorization (NMF) method that is suitable for decomposing the label matrix and finding a proper hidden space by a feature-aware approach. We consider that the label matrix is binary and also in this matrix some deserving labels for an instance may not be on (called missing labels). We conduct several experiments and show the superiority of our proposed methods to the state-of-the-art multi-label classification methods.

关键词： Multi-label Classification non negative matrix factorization Feature Aware

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MOTION INFORMED AUDIO SOURCE SEPARATION

MOTION INFORMED AUDIO SOURCE SEPARATION

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

作者： Parekh, Sanjeel Essid, Slim Ozerov, Alexey Duong, Ngoc Q. K. Perez, Patrick Richard, Gael Univ Paris Saclay Telecom ParisTech LTCI F-75013 Paris France Technicolor 975 Ave Champs BlancsCS 17616 F-35576 Cesson Sevigne France

ISBN: (纸本)9781509041176

In this paper we tackle the problem of single channel audio source separation driven by descriptors of the sounding object's motion. As opposed to previous approach es, motion is included as a soft-coupling constraint within the non negative matrix factorization framework. The proposed method is applied to a multi modal dataset of instruments in string quartet performance recordings where bow motion information is used for separation of string instruments. We show that the approach offers better source separation result than an audio-based baseline and the state-of-the-art multimodal-based approaches on these very challenging music mixtures.

关键词： audio source separation non negative matrix factorization motion multi modal analysis

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