1 research outputs found
AutoEmbedder: A semi-supervised DNN embedding system for clustering
Clustering is widely used in unsupervised learning method that deals with
unlabeled data. Deep clustering has become a popular study area that relates
clustering with Deep Neural Network (DNN) architecture. Deep clustering method
downsamples high dimensional data, which may also relate clustering loss. Deep
clustering is also introduced in semi-supervised learning (SSL). Most SSL
methods depend on pairwise constraint information, which is a matrix containing
knowledge if data pairs can be in the same cluster or not. This paper
introduces a novel embedding system named AutoEmbedder, that downsamples higher
dimensional data to clusterable embedding points. To the best of our knowledge,
this is the first research endeavor that relates to traditional classifier DNN
architecture with a pairwise loss reduction technique. The training process is
semi-supervised and uses Siamese network architecture to compute pairwise
constraint loss in the feature learning phase. The AutoEmbedder outperforms
most of the existing DNN based semi-supervised methods tested on famous
datasets.Comment: The manuscript is accepted and published in Knowledge-Based Syste