Keywords
Missing dataProjection (relational algebra)Set (abstract data type)Computer scienceData setData miningStatisticsArtificial intelligenceMathematicsAlgorithmMachine learning
Affiliated Institutions
Related Publications
Partial least squares regression and projection on latent structure regression (PLS Regression)
Abstract Partial least squares (PLS) regression ( a.k.a. projection on latent structures) is a recent technique that combines features from and generalizes principal component a...
Principal component analysis
Abstract Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter‐correlated quantitative d...
Time and memory efficient likelihood-based tree searches on phylogenomic alignments with missing data
Abstract Motivation: The current molecular data explosion poses new challenges for large-scale phylogenomic analyses that can comprise hundreds or even thousands of genes. A pro...
Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm
Abstract. The expectation‐maximization (EM) algorithm is a popular approach for obtaining maximum likelihood estimates in incomplete data problems because of its simplicity and ...
Model-Based Clustering, Discriminant Analysis, and Density Estimation
Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonab...
Publication Info
- Year
- 1996
- Type
- article
- Volume
- 35
- Issue
- 1
- Pages
- 45-65
- Citations
- 353
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
353
OpenAlex
Cite This
Philip R. Nelson,
Paul A. Taylor,
John F. MacGregor
(1996).
Missing data methods in PCA and PLS: Score calculations with incomplete observations.
Chemometrics and Intelligent Laboratory Systems
, 35
(1)
, 45-65.
https://doi.org/10.1016/s0169-7439(96)00007-x
Identifiers
- DOI
- 10.1016/s0169-7439(96)00007-x