Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neural-network-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.

We propose a novel density-based mode-seeking Hierarchical Quick Shift clustering algorithm with an optional Recurrent Neural Network (RNN) to jointly learn the cluster assignments for every sample and the underlying dynamics of the mode-seeking clustering process. As a mode-seeking clustering algorithm, Hierarchical Quick Shift constrains data samples to stay on similar trajectories. All data samples converging to the same local mode are assigned to a common cluster. The RNN enables us to learn quasi-temporal structures during the mode-seeking clustering process. It supports variable density clusters with arbitrary shapes without requiring the expected number of clusters a priori. We evaluate our method in extensive experiments to show the advantages over other density-based clustering algorithms.

A major goal of a Genome Wide Association Study (GWAS) is to find associations between genetic variations, such as Single-Nucleotide Polymorphisms (SNPs) and the risk for developing a complex disease, such as cancer or schizophrenia. Logic Feature Selection (logicFS) is a technique to search for interactions between SNPs possibly enhancing the risk to develop a particular disease. Composed of several hundreds of processors, the Graphics Processing Unit (GPU) has become a very interesting platform for computationally demanding tasks on massive data. A special hierarchy of processors and fast memory units allow very powerful and efficient parallelization but also demands novel parallel algorithms. In this paper, we formulate LogicFS-GPU algorithm particularly suited for the data parallel architectures, such as GPUs. For this purpose, we employ low (or device) level and high level data parallel primitives, e.g. map, compaction, parallel-prefix-sum (scan) and parallel reduction. The primary idea of our algorithm is to allow the parallel threads developing cooperatively their own private high quality binary interaction models to predict the affection status of subjects. We demonstrate (1) how to formulate the parallel LogicFS-GPU algorithm to be able to exploit most of the potential parallelism hidden in the base logicFS algorithm and (2) how to utilize the special memory and processor architecture of a modern GPU in order to share this information among threads in an optimal way. As a perspective, LogicFS-GPU is not limited examining SNP interactions, but can also be applied to any problem in which multi-variate binary predictor interactions are tried to be associated with observations. Furthermore, the target architecture of LogicFS-GPU is not only constrained by GPU and it may be possible to port our formulation to any other target data-parallel architecture.

Composed of several hundreds of processors, the Graphics Processing Unit (GPU) has become a very interesting platform for computationally demanding tasks on massive data. A special hierarchy of processors and fast memory units allow very powerful and efficient parallelization but also demands novel parallel algorithms. Expectation Maximization (EM) is a widely used technique for maximum likelihood estimation. In this paper, we propose an innovative EM clustering algorithm particularly suited for the GPU platform on NVIDIA's Fermi architecture. The central idea of our algorithm is to allow the parallel threads exchanging their local information in an asynchronous way and thus updating their cluster representatives on demand by a technique called Asynchronous Model Updates (Async-EM). Async-EM enables our algorithm not only to accelerate convergence but also to reduce the overhead induced by memory bandwidth limitations and synchronization requirements. We demonstrate (1) how to reformulate the EM algorithm to be able to exchange information using Async-EM and (2) how to exploit the special memory and processor architecture of a modern GPU in order to share this information among threads in an optimal way. As a perspective Async-EM is not limited to EM but can be applied to a variety of algorithms.