Multi-View Clustering via Deep Matrix Factorization
Authors: Handong Zhao, Zhengming Ding, Yun Fu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The superior experimental results on three face benchmarks show the effectiveness of the proposed deep matrix factorization model. We choose three face image/video benchmarks in our experiments, as face contains good structural information, which is beneficial to manifesting the strengths of deep NMF structure. |
| Researcher Affiliation | Academia | Handong Zhao, Zhengming Ding, Yun Fu Department of Electrical and Computer Engineering, Northeastern University, Boston, USA, 02115 College of Computer and Information Science, Northeastern University, Boston, USA, 02115 {hdzhao,allanding,yunfu}@ece.neu.edu |
| Pseudocode | Yes | Algorithm 1: Optimization of Problem (3) |
| Open Source Code | No | The paper does not provide any specific repository link or explicit statement about the release of the source code for the methodology described. |
| Open Datasets | Yes | We choose three face image/video benchmarks in our experiments... Yale consists of 165 images of 15 subjects... Extended Yale B consists of 38 subjects of face images... Notting-Hill is a well-known video face benchmark (Zhang et al. 2009)... |
| Dataset Splits | No | The paper mentions 'train' in the context of pre-training and optimization, and evaluates on 'test' data through the performance tables, but does not provide specific train/validation/test dataset split information (percentages, counts, or predefined splits) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | The corresponding parameters γ, β and layer size are set as 0.5, 0.1 and [100, 50], respectively. Parameter β is set as 0.1. γ is evaluated in the grid of {5 10 3, 5 10 2, 5 10 1, 5 100, 5 101, 5 102}. We fix parameter γ = 0.5 as default in our experiments. In practice, we choose β = 0.01 as default. For the layer size analysis, from Figure 3 and Figure 4, we observe that the setting of [100 50] always performs best. Empirically, we find that the last layer dimension usually plays a more important role than other layer size (blue curves are always close to red ones). |