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20th National Conference on Communications (NCC)

作者： Khare, Shreya Bhandari, Akshay Murthy, Hema A. Indian Inst Technol Dept Comp Sci & Engn Madras 600036 Tamil Nadu India

ISBN: (纸本)9781479923618

Internet availability on a campus is not metered. Internet link bandwidths are vulnerable as they can be misused. Moreover, websites blacklist campuses for misuse. Especially blacklisting by academic websites like IEEE and ACM can lead to serious researchers being denied access to information. The objective of this paper is to proactively classify anomalous accesses. This will enable campus ISPs to deny access to users, misusing the Internet. In particular URLs are classified using the short snippets(meta-data) that are available. New Features, namely random walk term weights, within class popularity in tandem with non negative matrix factorization show a lot of promise for classifying URLs. The classification accuracy is as a high as 92.96% on 10 gigabytes of proxy data.

关键词： Web Page Classification non negative matrix factorization

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A New Algorithm for Solving Data Sparsity problem Based-On non negative matrix factorization in Recommender Systems 4

A New Algorithm for Solving Data Sparsity problem Based-On N...

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4th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Sharifi, Zeinab Rezghi, Mansoor Nasiri, Mahdi Islamic Azad Univ Dept Comp Engn Sci & Res Branch Guilan Iran Univ Tarbiat Modares Dept Comp Sci Tehran Iran Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781479954865

The "sparsity" challenge is a well-known problem in recommender systems. This issue relates to little information about each user or item in large data set. The purpose of this paper is pre-processing of data and using non negative matrix factorization (NMF) method to improve this challenge. Since the original data are non negative, the algorithm based on NMF maintains positive effect of data on decomposition matrices and makes better prediction of original data in comparison to singular value decomposition (SVD) algorithm. Since the dimensions of data are very large, it offers a solution based on dimensionality reduction in which useful factors for selecting optimal dimensions (optimal 'k') are extracted from the data matrix until appropriate approximation of the original data obtains from rank 'k' matrices. Thus, the presented model not only selects the best factors from the original data but also, recommends appropriate values for the missing ratings and overcome sparsity problem. The results of experiments are evaluated with three metrics: RMSE1, NMAE(2), and RE3. Results show that our approach leads to better prediction.

关键词： Recommender System non negative matrix factorization Collaborative Filtering Data Sparsity

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SEMI-SUPERVISED VISUAL RECOGNITION WITH CONSTRAINED GRAPH REGULARIZED non negative matrix factorization

SEMI-SUPERVISED VISUAL RECOGNITION WITH CONSTRAINED GRAPH RE...

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20th IEEE International Conference on Image Processing (ICIP)

作者： Guo, Weiwei Hu, Weidong Boulgouris, Nikolaos V. Patras, Ioannis Natl Univ Def & Technol Changsha Hunan Peoples R China Brunel Univ Uxbridge UB8 3PH Middx England Univ London London WC1E 7HU England

ISBN: (纸本)9781479923410

This paper proposes a semi-supervised nonnegative matrix factorization algorithm for face and gait recognition. The proposed algorithm imposes hard constraints on the labelled data points, such that the data points that belong to the same class are projected to the same lower dimensional point. In addition, it introduces a graph Laplacian regularization term that preserves the local geometry structure of the data by penalising large distances between the projections of points that are close in the original space. This results in a constrained optimization problem, that is solved using block coordinate descent with multiplicative update rules. Experimental results on several publicly available datasets demonstrate that proposed method performs in par or considerably better than state of the art methods.

关键词： non negative matrix factorization Object Recognition Semi-supervised Learning

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exp2GO: Improving Prediction of Functions in the Gene Ontology With Expression Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第2期20卷 999-1008页

作者： Di Persia, Leandro Lopez, Tiago Arce, Agustin Milone, Diego H. Stegmayer, Georgina Consejo Nacl Invest Cient & Tecn FICH UNL Sinc I Res Inst Signals Syst & Computat Intelligence Ciudad Univ UNLS3000ADQ Santa Fe Argentina Consejo Nacl Invest Cient & Tecn UNL IAL Inst Agrobiotecnol Litoral Ciudad Univ UNLS3000ADQ Santa Fe Argentina

The computational methods for the prediction of gene function annotations aim to automatically find associations between a gene and a set of Gene Ontology (GO) terms describing its functions. Since the hand-made curation process of novel annotations and the corresponding wet experiments validations are very time-consuming and costly procedures, there is a need for computational tools that can reliably predict likely annotations and boost the discovery of new gene functions. This work proposes a novel method for predicting annotations based on the inference of GO similarities from expression similarities. The novel method was benchmarked against other methods on several public biological datasets, obtaining the best comparative results. exp2GO effectively improved the prediction of GO annotations in comparison to state-of-the-art methods. Furthermore, the proposal was validated with a full genome case where it was capable of predicting relevant and accurate biological functions. The repository of this project withh full data and code is available at https://***/sinc-lab/exp2GO.

关键词： Annotations Semantics Ontologies Genomics Bioinformatics Proteins Bayes methods Gene ontology semantic distance gene function prediction non negative matrix factorization bayesian inference

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Towards a Computational Approach to Quantification of Allele Specific Expression at Population Level 11th

Towards a Computational Approach to Quantification of Allele...

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11th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Pagliarini, Roberto Marroni, Fabio Piazza, Carla Gabelli, Giovanni Magris, Gabriele Di Gaspero, Gabriele Morgante, Michele Policriti, Alberto Univ Udine Dept Math Comp Sci & Phys Udine Italy Univ Udine Dept Agr Food Environm & Anim Sci Udine Italy Univ Padua Dept Agron Food Nat Resources Anim & Environm Padua Italy Inst Appl Genom Udine Udine Italy

ISBN: (纸本)9783031646355;9783031646362

Allelic Specific Expression (ASE) analysis aims to assess the difference in expression between the two alleles of the same gene in a heterozygous individual. Next-generation sequencing (NGS) technologies give the possibility to measure ASE of all the genes of a species in a population of individuals through RNA-sequencing (RNA-seq). ASE is usually reported only as a relative measure for individual genotypes. The ratio between the number of aligned reads on the two different haplotypes represents an indication of the allelic expression imbalance (AEI) between the two alleles. With the aim to understand the existing cis-regulatory variation within a population of individuals, the inference of the expression of each allele starting from these ratios represents a crucial goal. In the real case studies, only a small subset of these AEI are obtained from bulk RNA-seq data. This leads to the "Population Level Allelic Imbalance Problem". In this work, we will present a computational workflow to obtain a solution to this problem. First, a graph theoretical approach is applied to reconstruct the entire data-set of allele ratios. Then, non negative matrix factorization is employed to compute the allele values. A running example will be used to demonstrate how it works, and its soundness will be evaluated by inferring the allele values in genes of chromosome 1 of leaves in 98 cultivars representative of the variability present in Vitis vinifera.

关键词： Allelic Specific Expression Alleles Inference Allelic Expression Imbalance non negative matrix factorization

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Integrating matrix factorization with Graph based Models 24

Integrating Matrix Factorization with Graph based Models

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18h ACM Conference on Recommender Systems (RecSys)

作者： Mehta, Rachana PDEU Sch Technol Dept Comp Sci & Engn Gandhinagar Gujarat India

ISBN: (纸本)9798400705052

Graph based Recommender models make use of user-item rating and user-user social relationships to elicit recommendation performance by extracting inherent geometrical knowledge. In a social graph scenario, user-user trust plays a significant role in reducing sparsity and has varied characteristics that can be exploited. Existing models limit themselves to learning from either a highorder interaction graph of user-item ratings or a user-user social graph from trust value. They explore other trust characteristics in a very limited setting. The graph based model, designed using entire user-user social information, impacts performance and escalates complexities in model learning. To alleviate these issues of graph learning, graph recommender seeks assistance from matrix factorization techniques. Incorporating graph based model with matrix factorization brings its own set of challenges of model integration, leveraging trust, graph learning, and optimization. This article presents the existing work in that line and future possibilities and challenges to be catered to through novel developments

关键词： Recommender Systems Collaborative Filtering non negative matrix factorization Graph Neural Network Auxiliary Information

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SEMI-SUPERVISED VISUAL RECOGNITION WITH CONSTRAINED GRAPH REGULARIZED non negative matrix factorization

SEMI-SUPERVISED VISUAL RECOGNITION WITH CONSTRAINED GRAPH RE...

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IEEE International Conference on Image Processing

作者： Weiwei Guo Weidong Hu Nikolaos V. Boulgouris Ioannis Patras National University of Defense and Technology Brunel University Queen Mary University of London

ISBN: (纸本)9781479923427

关键词： non negative matrix factorization Object Recognition Semi-supervised Learning object recognition matrix decomposition state of the art Semi-supervised Learning factorization NOTIFICATION OF non-COMPLIANCE Visualization negative Constrained optimization

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A Voting based Assimilation Method for the Winning Neurons in Multi-Level SOM to Cluster the Convoluted Biomarkers of a Time Varying 'Omic Data 15

A Voting based Assimilation Method for the Winning Neurons i...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dey, Anirban Sharma, Kaushik Das Bhattacharjee, Pritha Chatterjee, Amitava University of Calcutta Department of Applied Physics Kolkata India University of Calcutta Department of Environmental Science Kolkata India Jadavpur University Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350370249

Early detection and modelling of disease development are crucial for effective healthcare. Diseases develop over time, with changes in 'omic expressions, leading to genotypic or phenotypic changes in the sample. These changes are unique to disease samples and can develop in various parts of the body and continue to grow, if not addressed in a timely fashion. However, due to the availability of a small population of very high dimensional 'omic samples, this presents a significant challenge. Thus, dimensionality reduction techniques are essential to simplify the data while preserving its core structure. This work focuses on the technique of non-negative matrix factorization (NMF), where the decomposed basis and coefficient matrices are component-wise non-negative. NMF has recently gained prominence for analyzing time-varying 'omic data and the present work uses a contemporary variant of NMF, called flexible NMF (FNMF). In a bid to effective extract differentially expressed biomarkers using NMF, this work proposes a voting based assimilation method for factorized convoluted biomarkers obtained from FNMF using a hybrid MLSOM (Hy-MLSOM) architecture. This results in an enhanced representation of the 'omic data, thereby facilitating more effective and precise disease detection. © 2024 IEEE.

关键词： Biomarker Identification Feature Representational Learning Microarray Data Analysis non negative matrix factorization Self-Organizing Maps

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Towards Sensing and Sharing Auditory Context Information Using Wearable Device 19th

Towards Sensing and Sharing Auditory Context Information Usi...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Sashima, Akio Kawamoto, Mitsuru Natl Inst Adv Ind Sci & Technol Human Augmentat Res Ctr Kashiwa Chiba Japan

ISBN: (纸本)9783030438876;9783030438869

Data-driven information services using wearable devices have attracted attention in the areas of healthcare, medical care, and educational services. In the services, the users' daily behaviors are modeled with the sensing data of the physical statuses of individual users, e.g., body movements, heart rates, etc. However, to understand human behaviors more deeply, it is also important to know the context information of the users, such as the surrounding environment and participating activities. In this paper, we describe extracting auditory context information from ambient sound data sensed by smart watches. First, we describe a prototype of our wearable ambient sound sensing system by using smart watches. Then, we describe an analysis of the sound data sensed by the system. We formalize the context extraction process as unsupervised segmentation of multi-dimensional time-series data and apply non-negative matrix factorization (NMF) and k-means clustering to the segmentation at the first step of the study. We confirm that the periods segmented by the analysis roughly correspond to actual contexts.

关键词： Smart watch Auditory context non negative matrix factorization

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Separation of human and animal seismic signatures using non-negative matrix factorization

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PATTERN RECOGNITION LETTERS 2012年第16期33卷 2085-2093页

作者： Mehmood, Asif Damarla, Thyagaraju Sabatier, James USA Res Lab Adelphi MD 20783 USA

Seismic footstep detection based systems can be employed for homeland security applications such as perimeter protection and the border security. This paper reports an approach based on non-negative matrix factorization (NMF) for seismic footstep signal separation for a single channel recording. A supervised NMF technique is employed to separate the human footstep signatures from the horse footstep signatures. The proposed algorithm is applied on the spectrogram of human footstep signals and horse footstep signals. The spectrograms of these signals are presented as a sum of components, each having a fixed spectrum and time-varying gain. The main benefit of the proposed technique is its ability to decompose a complex signal automatically into objects that have a meaningful interpretation. In this paper, a sparsity-based NMF algorithm is developed and implemented on seismic data of human and horse footsteps. The performance of this method is very promising and is demonstrated by the experimental results. (c) 2012 Elsevier B.V. All rights reserved.

关键词： non negative matrix factorization Dimensionality reduction Sparsity Single channel source separation Spectrogram

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