24/02/2021, 17:15 — 17:30 —
Rodrigo Girão Serrão, Universidade de Lisboa
Principal Component Analysis for Interval Valued Data
Principal Component Analysis is a well-known statistical method that is commonly used as a dimensionality reduction method. This method works with points in the R^n space, but how can we apply it to datasets containing other types of data? The intuitive ideas that shape Principal Component Analysis can be adapted with ease when we are dealing with interval data, but these adaptations often lack theoretical justification. Having said that, how can we transfer the intuition of Principal Component Analysis to this new domain in a way that is mathematically sound?
See also
rodrgo_gs_spca_interval_data.pdf
