A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Coupled Compound Poisson Factorization
[article]
2017
arXiv
pre-print
Preserved Fulltext
and factor analysis---to robustly model non-random missing data in the context of clustering, prediction, and matrix factorization. ...
In all three cases, we test our framework against models that ignore the missing-data mechanism on large scale studies with non-random missing data, and we show that explicitly modeling the missing-data ...
Probabilistic models for low-rank approximation of extremely sparse matrices are abundant, and include probabilistic matrix factorization (PMF) (Salakhutdinov & Mnih, 2011) , non-negative matrix factorization ...
arXiv:1701.02058v1
fatcat:3h27rc2pejhmjh6mzl6rvhq2be
Collaborative prediction and ranking with non-random missing data
2009
Proceedings of the third ACM conference on Recommender systems - RecSys '09
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper we present the first study of the effect of non-random missing data on collaborative ranking, and extend our previous results regarding the impact of non-random missing data on collaborative ...
The statistical theory of missing data shows that incorrect assumptions about missing data can lead to biased parameter estimation and prediction. ...
Matrix factorization We consider a probabilistic matrix factorization model with global mean offset as seen in Equations 21 to 23 [10] . ...
doi:10.1145/1639714.1639717
dblp:conf/recsys/MarlinZ09
fatcat:drndmqvloja7fhb6vpt6fdgwmm
Slides | Bayesian temporal factorization for multidimensional time series prediction
2021
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process into a single probabilistic graphical model, this framework can characterize both global and local consistencies ...
missing data imputation and multi-step rolling prediction tasks. ...
Missing data generation:
• Random missing (RM)
(Data are missing at random.)
T
2T
3T
4T
5T
6T
7T
• Non-random missing (NM)
(Data are missing continuously during a few time periods.) ...
doi:10.5281/zenodo.4693405
fatcat:tkogabg4kvex3mowjx7ypnykm4
Probabilistic Non-Negative Tensor Factorization Using Markov Chain Monte Carlo
2009
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
CONCLUSIONS We have presented a model for probabilistic non-negative matrix factorization. ...
elements in the likelihood term, which corresponds to dealing with the "missing at random" data assumption. ...
doi:10.5281/zenodo.41712
fatcat:ophf4getkrbfhksok2teyftvee
Fast nonparametric matrix factorization for large-scale collaborative filtering
2009
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2009; you can also visit the original URL.
The file type is application/pdf.
In this paper we develop nonparametric matrix factorization methods by allowing the latent factors of two low-rank matrix factorization methods, the singular value decomposition (SVD) and probabilistic ...
principal component analysis (pPCA), to be data-driven, with the dimensionality increasing with data size. ...
However (11) is not a probabilistic way to deal with uncertainties and missing data. ...
doi:10.1145/1571941.1571979
dblp:conf/sigir/YuZLG09
fatcat:wvjkf54or5fcznyhc4nidt7cxm
Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Latent variable models can be used to probabilistically "fill-in" missing data entries. ...
We show how to calculate exactly the latent posterior distribution for the factor analysis (FA) model in the presence of missing data, and note that this solution implies that a different encoder network ...
CN is supported by a PhD studentship from the EPSRC CDT in Data Science EP/L016427/1. CW thanks Kevin Murphy for a useful email discussion. ...
arXiv:1801.03851v3
fatcat:snapaakk4nblddmpxhtcmvvuja
Optimal Recovery of Missing Values for Non-negative Matrix Factorization
2021
IEEE Open Journal of Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We describe a straightforward unsupervised imputation algorithm, a minimax approach based on optimal recovery, and derive probabilistic error bounds on downstream non-negative matrix factorization (NMF ...
Missing values imputation is often evaluated on some similarity measure between actual and imputed data. ...
Non-negative matrix factorization (NMF) Matrix factorization is commonly used for clustering and dimensionality reduction in computational biology, imaging, and other fields. ...
doi:10.1109/ojsp.2021.3069373
fatcat:xzb5cz3j5rc45gb4lfbc3c4kdm
Non-linear matrix factorization with Gaussian processes
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
A popular approach to collaborative filtering is matrix factorization. In this paper we develop a non-linear probabilistic matrix factorization using Gaussian process latent variable models. ...
SGD allows us to apply Gaussian processes to data sets with millions of observations without approximate methods. We apply our approach to benchmark movie recommender data sets. ...
We then extend this model in a non-linear way to give a probabilistic non-linear matrix factorization. ...
doi:10.1145/1553374.1553452
dblp:conf/icml/LawrenceU09
fatcat:rkl3ehvatveohojdicxlhkw2vq
The probabilistic tensor decomposition toolbox
2020
Machine Learning: Science and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Acknowledgments The Probabilistic Tensor Toolbox was developed as part of the PhD thesis of the first author and supervised by both co-authors. ...
Figure 4 . 4 Tensor Completion: Data entries are missing at random (top row) or fibers are missing at random (bottom row). ...
factor matrix A (n) . ...
doi:10.1088/2632-2153/ab8241
fatcat:e3jyr3iahvgfdkmr6jlmqx3ixu
Survey on Probabilistic Models of Low-Rank Matrix Factorizations
2017
Entropy
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
approximation of a given data matrix. ...
The most significant difference between low-rank matrix factorizations and their corresponding probabilistic models is that the latter treat the low-rank components as random variables. ...
Probabilistic Models of Non-Negative Matrix Factorization Non-negative matrix factorization (NMF) decomposes a non-negative data matrix into the product of two non-negative low-rank matrices. ...
doi:10.3390/e19080424
fatcat:5joohnutojgidny6o3frsiezki
Missing Data in Kernel PCA
[chapter]
2006
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Despite its success and flexibility, the lack of a probabilistic interpretation means that some problems, such as handling missing or corrupted data, are very hard to deal with. ...
This in turn allows a principled approach to the missing data problem. Furthermore, this new approach can be extended to reconstruct corrupted test data using fixed kernel feature extractors. ...
G.S. gratefully acknowledges support from a BBSRC award "Improved processing of microarray data using probabilistic models". ...
doi:10.1007/11871842_76
fatcat:gyjkd2d4rjfe3ltldr7hxmbcgu
RECOVERY OF MISSING DATA USING ENHANCED PROBABILISTIC MATRIX FACTORIZATION
2020
International Journal of Engineering Applied Sciences and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
One of the most common ways to build a recommendation system is with matrix factorization, which finds ways to predict a user's rating for a specific product based on previous ratings and other users' ...
Motivated by the observation that incorporating user network information is not as effective as constraining the user feature vector with latent constraint similarity matrix, we developed Constrained Kernelized ...
., (2017) proposed recovery of missing data through probabilistic matrix factoristion. k-means algorithm is used to cluster the sensor data and through factorization, missing data has been recovered. ...
doi:10.33564/ijeast.2020.v05i02.045
fatcat:vyzjpio2sfdgnhtoyj5eibdiwu
A Survey of Tensor Factorization Frameworks on Audio Modelling
2014
International Journal of Applied Mathematics Electronics and Computers
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
This survey is about Tensor Factorization methods for audio modeling, which focuses on probabilistic latent tensor factorization and generalized coupled tensor factorization by expectation maximization ...
The matrix factorization had major impact on clustering, non-negative matrix factorization, latent semantic indexing, collaborative indexing and many other methods when considered as matrix factorization ...
One improvement over the NMF model is capturing temporal variations by deconvolution which is introduced by Smaragadis [9] with the name of Non-negative Matrix Factor Deconvolution(NMFD). { } { } { } { ...
doi:10.18100/ijamec.70262
fatcat:5ooutd6x3rbzvn6adtwupx76zy
Probabilistic matrix factorization from quantized measurements
2017
2017 International Joint Conference on Neural Networks (IJCNN)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We consider the problem of factorizing a matrix with discrete-valued entries as a product of two low-rank matrices. ...
Extension to the case of missing values is also discussed. The proposed methods are evaluated on simulated data, and on a real data set for recommender systems. ...
However, the optimization problem associated with matrix factorization is in general non-convex, so specific optimization techniques have to be devised to solve it. ...
doi:10.1109/ijcnn.2017.7965865
dblp:conf/ijcnn/BottegalS17
fatcat:fm4jd57gtfbepnnvbd2wncnmye
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation. ...
We show that our approach achieves faster convergence per iteration and timestep (wall-clock) than Gibbs sampling and non-probabilistic approaches, and do not require additional samples to estimate the ...
The non-probabilistic method starts overfitting from 20% missing values, leading to very high prediction errors. ...
arXiv:1610.08127v1
fatcat:iscci3gogngw5ewq3qjlvswzma
« Previous
Showing results 1 — 15 out of 41,665 results