updating

doc/src/DimRed/DimRed.do.txt

@@ -872,7 +872,7 @@ $\bm{z}_i$ with at most $l$ vectors, with $l << p$, defined by the
 orthogonal projection of the data onto the columns spanned by the
 eigenvectors of the covariance(correlations matrix).
 
-
+The proof which follows will be updated by mid January 2020.
 
 !split
 ===== Proof of the PCA Theorem  =====
@@ -1001,7 +1001,7 @@ This material will be added by mid January 2020.
 
 !split
 ===== Principal Component Analysis =====
-!bblock
+
 Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm.
 First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it.
 
@@ -1148,7 +1148,6 @@ complexity is $O(m \times d^2)+O(d^3)$, instead of $O(m \times n^2) + O(n^3)$, s
 previous algorithms when $d$ is much smaller than $n$.
 
 
-!eblock
 
 
 !split