challenge
as it may introduce uncertainties into the data analysis.
Recent advances in matrix completion have shown competitive
imputation performance when applied to many real-world domains.
However, there are two major limitations when applying
matrix completion methods to spatial data. First, they make a
strong assumption that the entries are missing-at-random, which
may not hold for spatial data. Second, they may not effectively
utilize the underlying spatial structure of the data. To address
these limitations, this paper presents a novel clustered adversarial
matrix factorization method to explore and exploit the underlying
cluster structure of the spatial data in order to facilitate effective
imputation. The proposed method utilizes an adversarial network
to learn the joint probability distribution of the variables and
improve the imputation performance for the missing entries that
are not randomly sampled.
more »
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Sparse Data Reconstruction, Missing Value and Multiple Imputation through Matrix Factorization
Social science approaches to missing values predict avoided, unrequested, or lost information from dense data sets, typically surveys. The authors propose a matrix factorization approach to missing data imputation that (1) identifies underlying factors to model similarities across respondents and responses and (2) regularizes across factors to reduce their overinfluence for optimal data reconstruction. This approach may enable social scientists to draw new conclusions from sparse data sets with a large number of features, for example, historical or archival sources, online surveys with high attrition rates, or data sets created from Web scraping, which confound traditional imputation techniques. The authors introduce matrix factorization techniques and detail their probabilistic interpretation, and they demonstrate these techniques’ consistency with Rubin’s multiple imputation framework. The authors show via simulations using artificial data and data from real-world subsets of the General Social Survey and National Longitudinal Study of Youth cases for which matrix factorization techniques may be preferred. These findings recommend the use of matrix factorization for data reconstruction in several settings, particularly when data are Boolean and categorical and when large proportions of the data are missing.
more » « less- Award ID(s):
- 1934843
- PAR ID:
- 10376578
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Sociological Methodology
- Volume:
- 53
- Issue:
- 1
- ISSN:
- 0081-1750
- Format(s):
- Medium: X Size: p. 72-114
- Size(s):
- p. 72-114
- Sponsoring Org:
- National Science Foundation
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