My question is whether the use of DimensionReduce, for a simple example with Method -> "PrincipalComponentsAnalysis" is the equivalent to non-detect PCA, i.e. PCA for data, some of which are known only as an interval.

For example

data = RandomVariate[MultinormalDistribution[{3, 3, 3}, 
{{1, -0.5, 0.5}, {-0.5, 1, -0.25}, {0.5, -0.25, 1}} ], 1000];

eData = EventData /@ (data  /. {{_?(# < 1 &), y_, z_} -> {{0, 1.}, y, z}, 
{x_, _?(# < 1 &) , z_} -> {x, {0, 1.}, z}});

redeData = DimensionReduce[eData, Method ->  "PrincipalComponentsAnalysis"];
redData = DimensionReduce[data, Method ->  "PrincipalComponentsAnalysis"];
ListPlot[{redData, redeData}]

Gives the result as

ReduceDimense for original data & EvenData

Regardless undetected data is only a small part of the file.

  • $\begingroup$ Can you please edit your question to clarify what is that you are asking? $\endgroup$ – rhermans Jun 28 '18 at 19:27

(Extended comment.)

It appears that DimensionReduce on the event data eData is going to use a feature extractor that puts eData in an 8-dimensional space, and then the reduction is done over that 8-dimensional representation.

enter image description here

Compare with the same results for data. It appears that data is not changed before the reduction.

enter image description here

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