Joint Maximum Purity Forest with Application to Image Super-Resolution
This work addresses the need for more effective random forest schemes in machine learning tasks, with applications to image super-resolution, though it appears incremental as it builds on existing random forest frameworks.
The authors tackled the problem of improving random forests for classification, clustering, and regression by proposing Joint Maximum Purity Forest (JMPF), which transforms features into a pre-clustered space using a rotation matrix, resulting in superior performance over state-of-the-art random-forest-based methods and image super-resolution algorithms on public benchmarks.
In this paper, we propose a novel random-forest scheme, namely Joint Maximum Purity Forest (JMPF), for classification, clustering, and regression tasks. In the JMPF scheme, the original feature space is transformed into a compactly pre-clustered feature space, via a trained rotation matrix. The rotation matrix is obtained through an iterative quantization process, where the input data belonging to different classes are clustered to the respective vertices of the new feature space with maximum purity. In the new feature space, orthogonal hyperplanes, which are employed at the split-nodes of decision trees in random forests, can tackle the clustering problems effectively. We evaluated our proposed method on public benchmark datasets for regression and classification tasks, and experiments showed that JMPF remarkably outperforms other state-of-the-art random-forest-based approaches. Furthermore, we applied JMPF to image super-resolution, because the transformed, compact features are more discriminative to the clustering-regression scheme. Experiment results on several public benchmark datasets also showed that the JMPF-based image super-resolution scheme is consistently superior to recent state-of-the-art image super-resolution algorithms.