Overview and Recent Advances in Partial Least Squares

By Roman Rosipal, Nicole Krämer. 2005

1,187 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Statistics and Probability

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Citations in the corpus

Transductive Multi-View Zero-Shot Learning (2015, background): “This discrepancy is caused when the projections learned from the 40 auxiliary classes are applied directly to project the Pig instances – what ‘hasTail’ (as well as the other 84 attributes) visually means is different now.”
Deep roto-translation scattering for object classification (2014, methodology): “s a linear operator to reduce the number of scattering coefﬁcients and decorrelate them, before computing an SVM classiﬁer. The feature selection is implemented with a partial least square regression [22,31,24]. A logarithm non-linearity is applied to scattering coefﬁcients in order to separate low ”
Global analytic solution of fully-observed variational Bayesian matrix factorization (2013, methodology): “, 2004) and partial least-squares (Worsley et al., 1997; Rosipal and Krämer, 2006) are also closely related to MF.”
DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection (2015, background): “Pls is one of a broad class of methods for modeling relations between sets of observed features by means of latent variables called components [41].”
Cluster Canonical Correlation Analysis (2014, methodology): “In this paper we present cluster canonical correlation analysis (cluster-CCA) for joint dimensionality reduction of two sets of data points.”
Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval (2016, methodology): “The scope (i.e., the top K TABLE 1 MAP Comparison of Different Methods on the Pascal VOC Dataset Methods Image query Text query Average PCA+PLS 0.2757 0.1997 0.2377 PCA+BLM 0.2667 0.2408 0.2538 PCA+CCA 0.2655 0.2215 0.2435 PCA+CDFE 0.2928 0.2211 0.2569 PCA+GMMFA 0.3090 0.2308 0.2699 GMMFA 0.2253 0.1”
Distributed Generators as Providers of Reactive Power Support—A Market Approach (2013, methodology): “In order to clarify this idea, consider the following example of a singlesolution selection, which uses the same assumptions and results as the example of POES selection presented above. a) Example of Single-Solution Selection: The residuals, resulting from the application of the NLPLSR in the POES ”
Learning Coupled Feature Spaces for Cross-Modal Matching (2013, methodology): “Recently, Partial Least Squares (PLS) [20] is also used for the cross-modal matching problem.”
Partial least squares regression and projection on latent structure regression (PLS Regression) (2010, background): “These relationships are explored in detail in Refs 16, 38–40, By contrast, BPLS regression is a symmetric technique and therefore is closely related to canonical correlation, but BPLS regression seeks to extract the variance common to X and Y whereas canonical correlation seeks linear combinations…”
Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval (2015, methodology): “Table I also shows that the supervised algorithms (such as the GMLDA and GMMFA) clearly outperform the unsupervised ones (the CCA, PLS and BLM).”
Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds (2011, methodology): “That is, the directions given by CCA (using the dummy matrix Y for group membership) are equivalent to Fisher’s discrimination directions [50,51].”
Linear Discrimination, Ordination, and the Visualization of Selection Gradients in Modern Morphometrics (2011, positive): “They are equivalent to the vectors obtained from a partial least squares analysis of the data matrix against a normalized dummy matrix representing group affiliation (PLS discrimination; see Barker and Rayens 2003; Boulesteix 2005; Rosipal and Krämer 2006).”