Large-Scale Multi-View Spectral Clustering via Bipartite Graph
Authors: Yeqing Li, Feiping Nie, Heng Huang, Junzhou Huang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on five benchmark datasets demonstrate the effectiveness and efficiency of the proposed method, where our method runs up to nearly 3000 times faster than the state-of-the-art methods. |
| Researcher Affiliation | Academia | University of Texas at Arlington, Arlington, TX, 76019, USA {yeqing.li@mavs.uta.edu, feipingnie@gmail.com, heng@uta.edu, jzhuang@uta.edu} |
| Pseudocode | Yes | Algorithm 1 Multi-view Spectral Clustering (MVSC) 1: Input: Data matrix of all views X(v) Rn d(v) for v 1 . . . V , Number of classes K, Number of salient points m, parameter r. 2: Output: Cluster labels Y of each data points, all salient points U and cluster labels of all salient points. 3: Generate m salient points using k-means on concatenate features; 4: Compute affinity matrix Z(v) of each view. 5: Compute Laplacian L(v) of each view; 6: Initialize a(v) = 1/K; 7: repeat 8: Compute G by using Eq. (14); 9: Update a(v) by using Eq. (8); 10: until Converges. 11: Treat each row of G as new representation of each data point and compute the clustering labels Y by using kmeans algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We conduct several experiments to evaluate the performance of the proposed methods on five benchmarks datasets. These datasets are summarized in Table 2. (Table 2 lists datasets like HW, Caltech-101 (Fei-Fei, Fergus, and Perona 2007), Reuters, NUS-WIDE-Object (Chua et al. July 8 10 2009), Animal with attributes (AWA), with associated URLs like https://archive.ics.uci.edu/ml/datasets/Multiple+Features for HW and http://lms.comp.nus.edu.sg/research/NUS-WIDE.htm for NUS.) |
| Dataset Splits | Yes | Five fold cross-validation is used and we report the mean purity, the mean NMI and the mean testing time. |
| Hardware Specification | Yes | All our experiments are conducted on a desktop computer with a 3.4GHz Intel Core i7 CPU and 12GB RAM, Mat Lab 2012a (64bit). |
| Software Dependencies | Yes | Mat Lab 2012a (64bit). |
| Experiment Setup | Yes | We search the parameter r in logarithm form (log10 r from 0.1 to 2 with step size 0.2. We also set m = 400 and construct 8-nearest-neighbour graph between raw All the experiments are repeated for 10 times and average results are reported. |