diff options
Diffstat (limited to 'report2')
| -rwxr-xr-x | report2/paper.md | 12 |
1 files changed, 7 insertions, 5 deletions
diff --git a/report2/paper.md b/report2/paper.md index 7099df8..6358445 100755 --- a/report2/paper.md +++ b/report2/paper.md @@ -115,7 +115,7 @@ original distance ranking compared to square euclidiaen metrics. Results can be observed using the `-m|--mahalanobis` when running evalution with the repository complimenting this paper. -COMMENT ON VARIANCE AND MAHALANOBIS RESULTS +**COMMENT ON VARIANCE AND MAHALANOBIS RESULTS** \begin{figure} \begin{center} @@ -166,15 +166,17 @@ through Jaccardian metric as: $$ d_J(p,g_i)=1-\frac{\sum\limits_{j=1}^N min(V_{p,g_j},V_{g_i,g_j})}{\sum\limits_{j=1}^N max(V_{p,g_j},V_{g_i,g_j})} $$ It is then possible to perform a local query expansion using the g\textsubscript{i} neighbors of -defined as $V_p=\frac{1}{|N(p,k_2)|}\sum\limits_{g_i\in N(p,k_2)}V_{g_i}$. We refer to $k_2$ since -we limit the size of the nighbors to prevent noise from the $k_2$ neighbors. The dimension k of the *$R^*$* -set will instead be defined as $k_1$:$R^*(g_i,k_1)$. +defined as: +$$ V_p=\frac{1}{|N(p,k_2)|}\sum\limits_{g_i\in N(p,k_2)}V_{g_i} $$. +We refer to $k_2$ since we limit the size of the nighbors to prevent noise +from the $k_2$ neighbors. The dimension k of the *$R^*$* set will instead +be defined as $k_1$: $R^*(g_i,k_1)$. The distances obtained are then mixed, obtaining a final distance $d^*(p,g_i)$ that is used to obtain the improved ranklist: $d^*(p,g_i)=(1-\lambda)d_J(p,g_i)+\lambda d(p,g_i)$. The aim is to learn optimal values for $k_1,k_2$ and $\lambda$ in the training set that improve top1 identification accuracy. -This is done through a simple **GRADIENT DESCENT** algorithm followed by exhaustive search to estimate +This is done through a simple multi-direction search algorithm followed by exhaustive search to estimate $k_{1_{opt}}$ and $k_{2_{opt}}$ for eleven values of $\lambda$ from zero(only Jaccard distance) to one(only original distance) in steps of 0.1. The results obtained through this approach suggest: $k_{1_{opt}}=9, k_{2_{opt}}=3, 0.1\leq\lambda_{opt}\leq 0.3$. |