Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
This work addresses the challenge of improving clustering performance for data with complex nonlinear structures, though it appears incremental as it builds on prior joint optimization methods by incorporating deep learning.
The paper tackles the problem of jointly performing dimensionality reduction and clustering by assuming a nonlinear transformation from latent space to data, proposing a deep neural network approach to learn clustering-friendly representations. Experiments on real datasets demonstrate the method's effectiveness.
Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtained via linear transformation of latent representations that are easy to cluster; but in practice, the transformation from the latent space to the data can be more complicated. In this work, we assume that this transformation is an unknown and possibly nonlinear function. To recover the `clustering-friendly' latent representations and to better cluster the data, we propose a joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN). The motivation is to keep the advantages of jointly optimizing the two tasks, while exploiting the deep neural network's ability to approximate any nonlinear function. This way, the proposed approach can work well for a broad class of generative models. Towards this end, we carefully design the DNN structure and the associated joint optimization criterion, and propose an effective and scalable algorithm to handle the formulated optimization problem. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.