AutoEncoder by Forest
This introduces a novel method for auto-encoding that could benefit applications requiring efficient and robust models, though it is incremental as it adapts existing tree techniques to a new task.
The paper tackles the problem of auto-encoding, typically done with deep neural networks, by proposing EncoderForest, a tree ensemble-based auto-encoder that achieves lower reconstruction error and faster training speed compared to DNN autoencoders.
Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder. We present a procedure for enabling forests to do backward reconstruction by utilizing the equivalent classes defined by decision paths of the trees, and demonstrate its usage in both supervised and unsupervised setting. Experiments show that, compared with DNN autoencoders, eForest is able to obtain lower reconstruction error with fast training speed, while the model itself is reusable and damage-tolerable.