Dimensionality Reduction via Matrix Factorization for Predictive Modeling from Large, Sparse Behavioral Data

By Jessica Clark, Foster J. Provost. 2015

4 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. David Martens
2. Yanou Ramon
3. Foster J. Provost
4. Theodoros Evgeniou
5. Yu Zhang
6. Emilio Carrizosa
7. Cynthia Rudin
8. Qiang Yang

Citations in the corpus

• Explaining Classification Models Built on High-Dimensional Sparse Data (2016, critical): “Further, dimensionality reduction via singular value decomposition often may not improve predictive performance (and often reduce it) (Clark & Provost, 2015), and is dubious for improving explainability in any case.”
• A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C (2019, background): “Note that, while feature selection and dimensionality reduction techniques could help tackle the high dimensionality of models from textual and behavioral data, previous research has demonstrated that these approaches result in models with worse generalization performance than the ones trained on th”