Self-Adapted Multi-Task Clustering
Authors: Xianchao Zhang, Xiaotong Zhang, Han Liu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several real data sets show the superiority of the proposed algorithm over traditional single-task clustering methods and existing multi-task clustering methods. |
| Researcher Affiliation | Academia | School of Software, Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian 116620, China xczhang@dlut.edu.cn, zxt.dut@hotmail.com, liu.han.dut@gmail.com |
| Pseudocode | Yes | Algorithm 1 SAMTC |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology. Footnote 1 provides a link to text data, not code. |
| Open Datasets | Yes | We use data sets Web KB4, 20News Groups and Reuters1 to construct the multi-task data sets in three typical cases (Table 1). Footnote 1: http://www.cad.zju.edu.cn/home/dengcai/Data/Text Data.html |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It describes the datasets used for clustering but not how they were partitioned for model training or validation in a supervised learning sense. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For SAMTC, the number of nearest neighbors k(t)s (s 6= t) is set by searching the grid {ceil( ns 2 hs ), ceil( ns hs ), min(ceil( 2 ns hs ), ns)}... For SAMTC and Ncut-SNN, the number of nearest neighbors k(t)t is set by searching the grid {ceil( nt 2 ht ), ceil( nt ht ), min(ceil( 2 nt ht ), nt 1)}. For SMKC and SAMTC, the Gaussian kernel bandwidth is the median Euclidean distance between the data points. For S-MBC, the Bregman divergence we choose is Euclidean distance. For LSSMTC, the parameter λ is searched from {0.1, 0.2, . . . , 0.9}, the dimensionality of the shared subspace is searched from {2, 4, 6, 8, 10}. For MTFC and MTRC, λ1 and λ2 are both searched from {2 10, 2 8, . . . , 2 2}. For SMBC and S-MKC, λ is searched from {0.1, 0.2, . . . , 1}. |