# Agglomerative Clustering with Threshold Optimization via Extreme Value Theory
## 5 Experimental Results
### ImageNet-Based Experiments  
For all our ImageNet experiments, we use the ResNet-101 network pre-trained on ILSVRC 2012 [62] data, which achieves a top-1 error rate of 20.69% on the ILSVRC 2012 validation set. While these features are no longer state-of-the-art on ImageNet, our focus is on clustering not classification performance. We extract features for samples from ILSVRC 2010 and 2012 as detailed in Section 4.3. We chose to use the 1000 dimensional logits, i.e., the features before Softmax activation, and reduce their dimensionality to 128 by PCA. The performance of our clustering algorithm compared to other approaches can be observed for different distributions in Table 2. While the parameter-dependent clustering algorithms need to know the actual number of clusters present in the data, they still cannot match the performance of some of the claimed parameter-independent approaches (FINCH) and our completely automatic approach (PEACH). The results depict that PEACH provides the best performance even when compared to parameter-dependent approaches that use ground-truth information to provide the same number of clusters as the number of classes. In addition, PEACH outperforms the best performing partition of FINCH, the current state of the art, and claimed parameter-free algorithm. The experiments on ImageNet also demonstrate the feasibility of our clustering approach to work with large-scale datasets. We use OLFW to test out-of-domain clustering, where we employ the same ILSVRC-2012 pre-trained deep feature extraction, but use these features to cluster faces. Results can be found in the last column in Table 1. PEACH outperforms PAHC even when PAHC uses ground-truth number of clusters. The heuristically-scaled version PEACH is the most robust to outliers and has the best performance. Additionally, PEACH is able to identify 1221 clusters, which is close to the 1680 subjects with more than one image, _even though the features were not trained on face data._ Note that FINCH uses PyFiann to find nearest neighbor, which results in slightly different NMIs each time, and we report what we actually obtain. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Algorithm** & & & & & & & & & & \\
**\# Subjects**\(\Rightarrow\) & \multicolumn{2}{c}{**256**} & \multicolumn{2}{c}{**512**} & \multicolumn{2}{c}{**1024**} & \multicolumn{2}{c}{**1845**} & \multicolumn{2}{c}{**1680 (5479)**} \\ \hline  & **NMI** & \(\mathbf{B^{3}}\) & **NMI** & \(\mathbf{B^{3}}\) & **NMI** & \(\mathbf{B^{3}}\) & **NMI** & \(\mathbf{B^{3}}\) & **NMI** & \(\mathbf{B^{3}}\) \\ \hline K-Means True K & 0.889 & 0.713 & 0.898 & 0.701 & 0.900 & 0.675 & 0.777 & 0.663 & 0.643 & 0.606 \\ DCC-NEG [2] & 0.926 & 0.816 & 0.921 & 0.802 & 0.922 & 0.751 & 0.925 & 0.746 & — & — \\ PAHC [1] & 0.865 & 0.648 & — & — & 0.890 & 0.639 & 0.890 & 0.610 & — & — \\ FINCH (Best Partition) [4] & 0.891 & 0.731 & 0.887 & 0.693 & 0.891 & 0.678 & 0.894 & 0.677 & 0.873 & 0.540 \\ FINCH (Closest to GT) [4] & 0.645 & 0.286 & 0.648 & 0.273 & 0.667 & 0.270 & 0.690 & 0.259 & 0.894 & 0.611 \\ \hline PET-FINCH & 0.876 & 0.599 & 0.888 & 0.608 & 0.892 & 0.594 & 0.892 & 0.576 & 0.894 & 0.611 \\ PEACH(\(\tau_{w}\)) & 0.940 & 0.842 & 0.941 & 0.814 & 0.943 & 0.817 & 0.941 & 0.821 & 0.945 & 0.783 \\ PEACH(\(\tau_{r}\)) & **0.960** & **0.907** & **0.956** & **0.886** & **0.956** & **0.875** & **0.956** & **0.870** & **0.957** & **0.860** \\ \hline \# Clusters PEACH (\(\tau_{r},\tau_{w}\)) & \multicolumn{2}{c}{_329, **251**} & \multicolumn{2}{c}{_643, **474**} & \multicolumn{2}{c}{_1298, **902**} & \multicolumn{2}{c}{_2278, **1817**} & \multicolumn{2}{c}{**1221, _1166_} \\ \(\Rightarrow\) & \multicolumn{2}{c}{} & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Clustering Out-of-Distribution/Out-of-Domain data._ This table shows NMI and \(B^{3}\) F-scores for various clustering algorithms on the IJB-B and LFW face datasets.