Very large scale classification based on K-Means clustering & Multi-Kernel SVM(SimpleMKL)
Here, we are going to implement the method proposed in this article, "Very large scale classification based on K-Means clustering & Multi-Kernel SVM(SimpleMKL)" at ACM Digital Library.
The code has below modules:
- KMeans Clustering
- Select nearest & furthest points of each cluster
- Duplicate Removal
- Remove all duplicate data
- Outlier Detection
- Remove the last ROT-data based on their outlier score
- Method proposed in this article, "Robust, Scalable Anomaly Detection for Large Collections of Images".
- Human Labeling
- Do labeling for the new representative dataset
- SimpleMKL
- Multi Kernel SVM
- Method proposed in this article, "Simplemkl".
The method is run on two diffrent types of datasets, large scale & very large scale satasets.
The large scale datasets are:
The very large scale datasets are:
- Coil2000 (9’822*85)
- Bank Marketing (45’211*17)
- Skin Segmentation (245’057*4)
- Covertype (581’012*54)
- Aspen vs other
Results can be seen at the end of presentation file uploaded in this repository.