SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
Authors: Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate the usefulness of our approach on synthetic and real datasets. In this section, we evaluate our approach on both synthetic (Section 5.1) and real datasets (Section 5.2 and Section 5.3). |
| Researcher Affiliation | Academia | 1Department of Computer Science, The University of Texas at Austin 2Department of Statistics, The University of Chicago 3Institute for Interdisciplinary Information Sciences, Tsinghua Univesity. |
| Pseudocode | Yes | Algorithm 1 Perm SMAC: Simultaneously mapping and clustering Input: Observation graph G = (S, E) and initial pairwise maps Xin ij , (i, j) E Output: Underlying clusters S = c1 ck and optimized pairwise maps Xij, 1 i, j n 1: {Step 1} Simultaneously compute the intra-cluster maps and extract the underlying clusters: 2: {Step 1.1} Form data matrix based on (1). 3: {Step 1.2} Compute the critical value r = argmax 2 i nm λi λi+1 λi+λi+1 . 4: {Step 1.3} Let U Rnm r store the leading r eigenvectors of X. Compute pair-wise maps X ij by solving (2) 5: {Step 1.4} Use fij(X ij) as the affinity score and apply single-linkage clustering to obtain the underlying clusters 6: {Step 2} compute the inter-cluster maps by solving (6) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We validate the map consistency argument through a motivating example (see Figure 1) on a real dataset from SHREC07 Watertight benchmark (Giorgi et al., 2007). The second dataset is more fine-grained. It has 10 underlying shape collections from FAUST training dataset (Bogo et al., 2014). |
| Dataset Splits | No | The paper uses 'SHREC07 Watertight benchmark' and 'FAUST training dataset' but does not provide specific train/validation/test dataset splits or percentages for its experiments. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions various methods and tools used for analysis (e.g., SIFT flow, spectral clustering) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes some parameters for synthetic data generation (e.g., number of vertices, points per object) and data processing steps for real datasets (e.g., sampling points, SIFT features), but it does not specify concrete hyperparameter values or system-level training settings for the SMAC algorithm itself, such as learning rates, batch sizes, or optimizer settings. |