Stochastic Unsupervised Learning on Unlabeled Data

In this paper, we introduce a stochastic unsupervised learning method that was used in the 2011 Unsupervised and Transfer Learning (UTL) challenge. This method is developed to preprocess the data that will be used in the subsequent classification problems. Specifically, it performs K-means clustering on principal components instead of raw data to remove the impact of noisy/irrelevant/less-relevant features and improve the robustness of the results. To alleviate the overfitting problem, we also utilize a stochastic process to combine multiple clustering assignments on each data point. Finally, promising results were observed on all the test data sets. Indeed, this proposed method won us the second place in the overall performance of the challenge.