Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence
This work addresses the challenge of enhancing clustering tasks for machine learning practitioners by leveraging external evidence, though it appears incremental as it builds on existing autoencoder and clustering methods.
The paper tackles the problem of improving clustering outcomes by introducing evidence transfer, a method that manipulates latent representations in an autoencoder using external categorical evidence, and shows it effectively enhances clustering when presented with real evidence while remaining robust to low-quality evidence.
In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical outcome of an external, auxiliary task is exploited to improve a primary task, in this case representation learning for clustering. Our proposed method makes no assumptions regarding the categorical evidence presented, nor the structure of the latent space. We compare our method, against the baseline solution by performing k-means clustering before and after its deployment. Experiments with three different kinds of evidence show that our method effectively manipulates the latent representations when introduced with real corresponding evidence, while remaining robust when presented with low quality evidence.