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Increase Robustness SDAE with Imputing Missing Value To Eliminate Users Sparse Data in Case E-Commerce Recommender System

Hanafi ., Nanna Suryana, Abd Samad Hasan Basari
2018 International Journal of Engineering & Technology  
Several efforts have been conducting to handle product sparse rating, however they fail to generate product recommendation accurately when face extreme sparse data, such as matrix factorization family  ...  that is not too sparse.  ...  Refer to our observations use table 4 on below, the performance of NN-SDAE is outperform than the NMF (Non-negative Matrix Factorization).  ... 
doi:10.14419/ijet.v7i4.44.26969 fatcat:x6s2o6v2jzc25aikkvckzqlxgy

VIPE: A new interactive classification framework for large sets of short texts - application to opinion mining [article]

Wissam Siblini and Frank Meyer and Pascale Kuntz
2018 arXiv   pre-print
Using a fast matrix factorization, the algorithm is able to handle large corpora and is well-adapted to interactivity by integrating the corrections proposed by the users on the fly.  ...  This paper presents a new interactive opinion mining tool that helps users to classify large sets of short texts originated from Web opinion polls, technical forums or Twitter.  ...  The fast factorization approach The matrix factorization allows to approximate the sparse × matrix by a full low-rank matrix = where ∈ ℝ × and ∈ ℝ × are respectively -dimensional ( ≪ and ≪ ) latent representations  ... 
arXiv:1803.02101v1 fatcat:t4lja2jvlvbufcdgitm4ikhdem

Outlier Detection for Text Data [chapter]

Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park
2017 Proceedings of the 2017 SIAM International Conference on Data Mining  
In this paper, we present a matrix factorization method, which is naturally able to distinguish the anomalies with the use of low rank approximations of the underlying data.  ...  The problem of outlier detection is extremely challenging in many domains such as text, in which the attribute values are typically non-negative, and most values are zero.  ...  We will relate the BCD framework to our non-negative matrix factorization problem, and explain our algorithm Text Outliers using Nonnegative Matrix Factorization(TONMF) in detail.  ... 
doi:10.1137/1.9781611974973.55 dblp:conf/sdm/KannanWAP17 fatcat:khzjklon2jhinjkqwatnnzziye

NE-UserCF: Collaborative Filtering Recommender System Model based on NMF and E2LSH

Yun Wu
2017 International Journal of Performability Engineering  
Non-negative Matrix Factorization algorithm (NMF) is a matrix factorization algorithm which finds the positive factorization of a given positive matrix.  ...  Then use E 2 LSH to cluster users in new-URM based on their interests and produce the similar-interest-user matrix (SIUM).  ...  Reference source not found. non-negative matrix) into two small matrixes with non-negative elements.  ... 
doi:10.23940/ijpe.17.05.p6.610619 fatcat:nhfpu4ulwzaxdf43lljtinhyla

Non-Negative Residual Matrix Factorization with Application to Graph Anomaly Detection [chapter]

Hanghang Tong, Ching-Yung Lin
2011 Proceedings of the 2011 SIAM International Conference on Data Mining  
We propose NrMF, a non-negative residual matrix factorization framework, to address such challenges. We present an optimization formulation as well as an effective algorithm to solve it.  ...  The low-rank approximation is usually presented in a factorized form e.g., A =à + R = FG + R where F, G are the factorized matrices of rank-r, and R is the residual matrix.  ...  Future research directions include (1) extending AltQP-Inc to time-evolving graphs, and (2) parallelizing AltQP-Inc using Hadoop 7 .  ... 
doi:10.1137/1.9781611972818.13 dblp:conf/sdm/TongL11 fatcat:dcjy6q5r5vfpfmjgvxr3faua64

Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization

Pedro Carmona-Saez, Roberto D Pascual-Marqui, F Tirado, Jose M Carazo, Alberto Pascual-Montano
2006 BMC Bioinformatics  
Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets.  ...  The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets.  ...  The new method, here referred to as Non-smooth Non-Negative Matrix Factorization (nsNMF) [23] , differs from the original in the use of an extra smoothness matrix to impose sparseness.  ... 
doi:10.1186/1471-2105-7-78 pmid:16503973 pmcid:PMC1434777 fatcat:whgqxq4nzrexxpsu6s5hbbn54m

Frontiers in Nonparametric Statistics

Peter Bühlmann, Tony Cai, Axel Munk, Bin Yu
2012 Oberwolfach Reports  
Examples include non-negative matrix factorization and he mainly focused on non-negative least squares in regression.  ...  Jianqing Fan (Princeton University) considered a multi-factor model for estimating a high-dimensional covariance matrix that is a sum of a low-rank matrix and a sparse matrix.  ... 
doi:10.4171/owr/2012/14 fatcat:ndg63i4x4vaqnpjitstzxrsh6a

Hierarchical Compound Poisson Factorization [article]

Mehmet E. Basbug, Barbara E. Engelhardt
2016 arXiv   pre-print
Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative  ...  HCPF can capture binary, non-negative discrete, non-negative continuous, and zero-inflated continuous responses.  ...  One alternative to PCA, non-negative matrix factorization (NMF), was first developed for factorizing matrices for face recognition (Lee & Seung, 1999) .  ... 
arXiv:1604.03853v2 fatcat:vqtfqczmhzgc5jjvxregflvu5i

ASAP: Automatic Semantics-Aware Analysis of Network Payloads [chapter]

Tammo Krueger, Nicole Krämer, Konrad Rieck
2011 Lecture Notes in Computer Science  
Security research has largely focused on network analysis using protocol specifications, for example for intrusion detection, fuzz testing and forensic analysis.  ...  We demonstrate the efficacy of semantics-aware analysis in different security applications: automatic discovery of patterns in honeypot data, analysis of malware communication and network intrusion detection  ...  Non-negative Matrix Factorization. In NMF, the orthogonality constraints are replaced by the requirement that the matrix B and C only contain non-negative entries.  ... 
doi:10.1007/978-3-642-19896-0_5 fatcat:rrotphkukrgvvgafj2flw2j44e

Temporal QoS-aware web service recommendation via non-negative tensor factorization

Wancai Zhang, Hailong Sun, Xudong Liu, Xiaohui Guo
2014 Proceedings of the 23rd international conference on World wide web - WWW '14  
Further, we formalize this problem as a generalized tensor factorization model and propose a Non-negative Tensor Factorization (NTF) algorithm which is able to deal with the triadic relations of user-service-time  ...  The comprehensive experimental analysis shows that our approach achieves better prediction accuracy than other approaches.  ...  However, the QoS value is non-negative, the conventional TF method dose not guarantee the result factors for non-negativity.  ... 
doi:10.1145/2566486.2568001 dblp:conf/www/ZhangSLG14 fatcat:3eisehcw3bbunk4rmmfhboh5lm

BaG: Behavior-aware Group Detection in Crowded Urban Spaces using WiFi Probes

Jiaxing Shen, Jiannong Cao, Xuefeng Liu
2019 The World Wide Web Conference on - WWW '19  
The group information is an important social context which could facilitate a more comprehensive behavior analysis.  ...  We propose a detection method based on collective matrix factorization to reveal the hidden associations by factorizing mobility information and usage patterns simultaneously.  ...  Collective Matrix FactorizationConstraints: Nonnegative & sparse Connectivity matrix NoB matrix Mobility User Behavior Mobility User Behavior W P H A B 1 2 Cluster 1 Cluster 2  ... 
doi:10.1145/3308558.3313590 dblp:conf/www/ShenCL19 fatcat:afxo56mjz5g4znoo6f34k7rpxi

Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization [article]

Joshua C. Chang, Patrick Fletcher, Jungmin Han, Ted L. Chang, Shashaank Vattikuti, Bart Desmet, Ayah Zirikly, Carson C. Chow
2020 arXiv   pre-print
For such data, hierarchical Poisson matrix factorization (HPF) and other sparse probabilistic non-negative matrix factorization (NMF) methods are considered to be interpretable generative models.  ...  Due to the lack of encoder sparsity, HPF does not possess the column-clustering property of classical NMF -- the factor loading matrix does not sufficiently define how each factor is formed from the original  ...  We also thank Amazon Web Services for providing computational resources and Boost Labs LLC for helping with visualization.  ... 
arXiv:2012.04171v3 fatcat:yulmyqh2fbfm5pfebahuuio6ya

From $K$-Means to Higher-Way Co-Clustering: Multilinear Decomposition With Sparse Latent Factors

Evangelos E. Papalexakis, Nicholas D. Sidiropoulos, Rasmus Bro
2013 IEEE Transactions on Signal Processing  
This paper starts from -means and shows how co-clustering can be formulated as a constrained multilinear decomposition with sparse latent factors.  ...  A basic multi-way co-clustering algorithm is proposed that exploits multilinearity using Lasso-type coordinate updates.  ...  We also assume non-negative data and impose non-negativity on the latent factors.  ... 
doi:10.1109/tsp.2012.2225052 fatcat:hyjebqtxwrgf5jercwyjzj5fba

Outlier Detection for Text Data : An Extended Version [article]

Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park
2017 arXiv   pre-print
In this paper, we present a matrix factorization method, which is naturally able to distinguish the anomalies with the use of low rank approximations of the underlying data.  ...  The problem of outlier detection is extremely challenging in many domains such as text, in which the attribute values are typically non-negative, and most values are zero.  ...  The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paidup, irrevocable, world-wide  ... 
arXiv:1701.01325v1 fatcat:ujl4qkjqdjasdosoprxbuhztv4

Discussion on Damping Factor Value in PageRank Computation

Atul Kumar Srivastava, Rakhi Garg, P. K. Mishra
2017 International Journal of Intelligent Systems and Applications  
So we have to choose some value of damping factor other than 0.85. In this paper, we have given an experimental analysis of PageRank computation for different value of the damping factor.  ...  Web search engines use various ranking methods to determine the order of web pages displayed on the Search Engine Result Page (SERP). PageRank is one of the popular and widely used ranking method.  ...  It is a matrix whose entries in each row are non-negative real numbers and sum equal to one. Aperiodic: A state is said to be periodic with period if is the smallest number such that all paths starting  ... 
doi:10.5815/ijisa.2017.09.03 fatcat:m2vmuchrx5d7zmbnjfzxvuxzzm
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