ascopubs.org: Evaluating Dimensionality Reduction for Patient-Reported Outcome–Based Survival Modeling in Patients With Head and Neck Cancer
This study aims to improve survival modeling in head and neck cancer (HNC) by integrating patient-reported outcomes (PROs) using dimensionality reduction techniques. PROs capture symptom severity ...
Evaluating Dimensionality Reduction for Patient-Reported Outcome–Based Survival Modeling in Patients With Head and Neck Cancer
So, the dimensionality reduction (ignoring years) is clearly best. However, if it turns out that you are in an inflationary periods, not so good monthly seasonal adjustment. However, a year model may capture the inflation trend and produce better results. So which model to use, collapsed or full?
machine learning - Why is dimensionality reduction used if it almost ...
And Dimensionality reduction is also projection to a (hopefuly) meaningful space. But dimensionality reduction has to do so in a uninformed way -- it does not know what task you are reducing for. This is especially true for classification, where you have outright supervised information.
Why is t-SNE not used as a dimensionality reduction technique for ...
I learned that it's common to do dimensionality reduction before clustering. But, is there any situation that it is better to do clustering first, and then do dimensionality reduction?
Besides the properties listed, are there other crucial aspects to consider in the dimensionality reduction process, particularly for financial time series? Insights, experiences, and suggestions including theoretical advice, practical applications, or software/tool recommendations, are highly appreciated.
However, it can also be performed via singular value decomposition (SVD) of the data matrix $\mathbf X$. How does it work? What is the connection between these two approaches? What is the relationship between SVD and PCA? Or in other words, how to use SVD of the data matrix to perform dimensionality reduction?