From 6af39548a765109ca94ac8162eec1aee7828b8c3 Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Thu, 6 Sep 2012 16:41:55 -0700
Subject: minor updates

---
 uniqueness.tex | 36 +++++++++++++++---------------------
 1 file changed, 15 insertions(+), 21 deletions(-)

(limited to 'uniqueness.tex')

diff --git a/uniqueness.tex b/uniqueness.tex
index ca89e85..429e7de 100644
--- a/uniqueness.tex
+++ b/uniqueness.tex
@@ -30,15 +30,14 @@ setting in the LFW benchmark.
 
 \subsection{Experiment design}
 
-In order to run an experiment similar to the one used in the face
-pair-matching problem (Section~\ref{sec:frb}), we use the Goldman
-Osteological Dataset \cite{deadbodies}. This dataset consists of
-skeletal measurements of 1,538 skeletons uncovered around the world and dating
-from the modern geological era. Given the way this data was collected, only a
-partial view of the skeleton is available.  We keep six measurements: the
-lengths of four bones (radius, humerus, femur, and tibia) and the breadth and
-height of the pelvis.  Because of missing values, this reduces the size of the
-dataset to 1,191.
+In order to run an experiment similar to the one used in the face pair-matching
+problem (Section~\ref{sec:frb}), we use the \emph{Goldman Osteological Dataset}
+\cite{deadbodies}. This dataset consists of skeletal measurements of 1,538
+skeletons uncovered around the world and dating from the modern geological era.
+Given the way this data was collected, only a partial view of the skeleton is
+available.  We keep six measurements: the lengths of four bones (radius,
+humerus, femur, and tibia) and the breadth and height of the pelvis.  Because
+of missing values, this reduces the size of the dataset to 1,191.
 
 From this dataset, 1,191 matched pairs and 1,191 unmatched pairs are
 generated.  In practice, the exact measurements of the bones of living
@@ -76,18 +75,13 @@ output of the algorithm for the threshold $\delta$ is defined as:
   \label{fig:roc}
 \end{figure}
 
-Figure \ref{fig:roc} shows the ROC curve of the nearest neighbor
-algorithm for different values of the standard deviation of the noise,
-as well as the ROC of the best performing face detection algorithm in
-the image-restricted LFW benchmark: \emph{Associate-Predict}
-\cite{associate}.
-
-The results show that with a standard deviation of 3 millimeters, nearest
-neighbor performs quite similarly to face detection at low
-false-positive rate. At this noise level, the error is smaller than
-1 centimeter with 99.9\% probability. Even with a standard deviation of 5 millimeters, it
-is still possible to detect 90\% of the matched pairs with a false
-positive rate of 6\%.
+Figure \ref{fig:roc} shows the ROC curve of the nearest neighbor algorithm for
+different values of the standard deviation of the noise.  The results show that
+with a standard deviation of 3 millimeters, nearest neighbor performs quite
+similarly to face detection at low false-positive rate. At this noise level,
+the error is smaller than 1 centimeter with 99.9\% probability. Even with a
+standard deviation of 5 millimeters, it is still possible to detect 90\% of the
+matched pairs with a false positive rate of 6\%.
 
 This experiment gives an idea of the noise variance level above which
 it is not possible to consistently distinguish skeletons. If the noise
-- 
cgit v1.2.3-70-g09d2

