Multi-Task Clustering with Model Relation Learning
Authors: Xiaotong Zhang, Xianchao Zhang, Han Liu, Jiebo Luo
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multi-task clustering methods. |
| Researcher Affiliation | Academia | Xiaotong Zhang1,2, Xianchao Zhang1,2, Han Liu1,2, Jiebo Luo3 1 School of Software, Dalian University of Technology 2 Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province 3 Department of Computer Science, University of Rochester zxt.dut@hotmail.com, xczhang@dlut.edu.cn, liu.han.dut@gmail.com, jluo@cs.rochester.edu |
| Pseudocode | Yes | Algorithm 1 MTCMRL |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | Web KB41: This data set contains web pages collected from computer science department websites at 4 universities: Cornell, Texas, Washington and Wisconsin. 1http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/ ; 20News Groups2: This data set consists of the news documents under 20 categories... 2http://qwone.com/ jason/20Newsgroups/ ; Reuters3: This data set is composed of the news documents under 135 categories from the Reuters newswire... 3http://www.cad.zju.edu.cn/home/dengcai/Data/Text Data.html |
| Dataset Splits | No | The paper does not specify train/validation/test dataset splits or cross-validation setup for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run experiments. |
| Software Dependencies | No | The paper mentions using 'QUAD function in MATLAB' and 'LYAP function in MATLAB' but does not specify version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | We investigate the impact of the parameters λ, µ, α and β on the clustering performance for MTCMRL. For each data set, we repeat the MTCMRL method 10 times by setting one parameter to search the grid and fixing the other parameters, then compute the average Acc and NMI of all the tasks under this parameter. More specifically, we successively set one parameter to search the grid {2-2, 2-1, 20, 21, 22} by fixing λ, α = 22 and µ, β = 2-1. ... For MTCMRL, we set λ and α to search the grid {2-2, 2-1, 20, 21, 22}, µ = 2-1, β = 2-1, and we choose cosine similarity to compute the similarity matrix. ... Initializing Y t(t = 1, . . . , T ) by the k-means method, then setting Y t = Y t + 0.2. Initializing W t(t = 1, . . . , T ) as an nt ht matrix of ones. |