Simon revision

[psolsson]

Should address #154, #145, #144 and #136 partially.

[brookehus]

I agree that I don't think we need operators. Maybe adding the dimensions of the entities in the equations would be helpful because of the transposes (m timescales, n features or something). And clarifying why lambdas fet (tau)s too.

Am 06.09.2018 um 07:47 schrieb Simon Olsson notifications@github.com:

@psolsson commented on this pull request.

In manuscript/manuscript.tex:

@@ -270,7 +282,7 @@ \subsection{Feature selection}

Here, we utilize the VAMP-2 score, which maximizes the kinetic variance contained in the features~\cite{kinetic-maps}. We should always evaluate the score in a cross-validated manner to ensure that we neither include too few features (under-fitting) or too many features (over-fitting)~\cite{gmrq,vamp-preprint}. -To choose among three different molecular features relevant to protein structure, we compute the (cross-validated) VAMP-2 score at a lag time of~$0.5$~ns. +To choose among three different molecular features reflecting protein structure, we compute the (cross-validated) VAMP-2 score (Notebook 00). Although we cannot optimize lag times with a variational score, such as VAMP-2, it is important to ensure that properties that we optimize are robust as a function of lag time. Consequently, we compute the VAMP-2 score at several lag times (Notebook 00). We find that the relative rankings of the different molecular features are highly robust as a function of lag time. We show one example of this ranking and the absolute VAMP-2 scores for lag time~$0.5$~ns in Fig.~\ref{fig:io-to-tica}b. Thanks for the clarification.

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[psolsson]

@brookehus I think I will leave this as is for now. as far as I understand this is formally correct, but I think the over-all theory section needs some re-organization. I think @franknoe will look over this early next week.

I agree that I don't think we need operators. Maybe adding the dimensions of the entities in the equations would be helpful because of the transposes (m timescales, n features or something). And clarifying why lambdas fet (tau)s too.
…

 Am 06.09.2018 um 07:47 schrieb Simon Olsson ***@***.***>:

 @psolsson commented on this pull request.

 In manuscript/manuscript.tex:

 > @@ -270,7 +282,7 @@ \subsection{Feature selection}

  Here, we utilize the VAMP-2 score, which maximizes the kinetic variance contained in the features~\cite{kinetic-maps}.
  We should always evaluate the score in a cross-validated manner to ensure that we neither include too few features (under-fitting) or too many features (over-fitting)~\cite{gmrq,vamp-preprint}.
 -To choose among three different molecular features relevant to protein structure, we compute the (cross-validated) VAMP-2 score at a lag time of~$0.5$~ns.
 +To choose among three different molecular features reflecting protein structure, we compute the (cross-validated) VAMP-2 score (Notebook 00). Although we cannot optimize lag times with a variational score, such as VAMP-2, it is important to ensure that properties that we optimize are robust as a function of lag time. Consequently, we compute the VAMP-2 score at several lag times (Notebook 00). We find that the relative rankings of the different molecular features are highly robust as a function of lag time. We show one example of this ranking and the absolute VAMP-2 scores for lag time~$0.5$~ns in Fig.~\ref{fig:io-to-tica}b.
 Thanks for the clarification.

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