Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics
Authors: Yining Wang, Jun Zhu
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that DP-space outperforms various competitors in terms of clustering accuracy and at the same time it is highly efficient. We compare DP-space with several competitors on both synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Yining Wang EMAIL Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA Jun Zhu EMAIL Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList, CBICR Center, Tsinghua University, China |
| Pseudocode | Yes | Algorithm 1 The DP-space algorithm |
| Open Source Code | No | The paper refers to code for *other* subspace clustering algorithms and implementations used for comparison, but does not provide access to the source code for their own DP-space method. |
| Open Datasets | Yes | We compare DP-space with several competitors on both synthetic and real-world datasets. The dataset contains 10, 000 data points from R3. Table 2. Average classification error (%) and running time (seconds) on the Hopkins-155 dataset. |
| Dataset Splits | Yes | Parameters of the DP-space algorithm are selected using 30% of the ground-truth labels according to NMI. The other hyper-parameters are selected via cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper states 'All methods are implemented in Matlab', but does not provide specific version numbers for Matlab or any other software libraries used. |
| Experiment Setup | Yes | Throughout the experiments we always set the hyper-parameter a to be 1, under which no shrinkage on lj is imposed. Values of λ range from 10 3 to 102 and values of s range from 10 2 to 102. We run the EM-MPPCA algorithm using 10 random initializations on each video sequence. |