A recent paradigm called Regeneration Learning addresses generative problems where the target data (e.g., images) is more complex than the available input source. While current cross-modal representation and regeneration learning rely on supervised deep learning models, this paper aims to revisit the adequacy of unsupervised models in this field. In this regard, we propose a new unsupervised approach that utilizes the SOM as a heteroassociative memory model to learn cross-modal representations in a topologically coherent map. This approach enables bidirectional predictive/regenerative mapping between domains. We evaluate the potential of this method on an unsolved (so far!) practical problem in petroleum geoscience.

In remote data collection from sampling stations, a vehicle must be within sufficient distance from the particular station for a predefined minimal time to retrieve all the required data from the site.

The planning task is to find a cost-efficient data collection trajectory, allowing the data collection vehicle to retrieve data from all sensing sites.

Having a fixed-wing aerial vehicle flying with a constant forward velocity, the problem is to determine the shortest feasible path that visits every sensing site and the vehicle is within a reliable communication distance from the station in a sufficient period.

We propose to formulate the planning problem as a~variant of the Close Enough Dubins Traveling Salesman Problem with Time Constraints (CEDTSP-TC) that is heuristically solved by unsupervised learning of the Growing Self-Organizing Array (GSOA) modified to address the minimal required time for the vehicle to be within the communication range of the station.

The proposed method is compared with a baseline based on a sampling-based decoupled approach.

The presented results support the feasibility of both proposed solvers on random instances and show that the GSOA-based approach outperforms the decoupled approach or provides similar results.

In this paper we propose the application of min-max-neurons for the use in generalized learning vector quantization (GLVQ) models, which correspond to min-max-prototypes. These prototypes can be identified with hyperboxes in the data space. Keeping the general GLVQ cost function, we redefine the Hebb-responsibilities for min-max-prototypes and derive consistent learning rules for stochastic gradient descent learning. We demonstrate that the resulting hyperbox-based GLVQ is capable to solve several classification tasks in robust manner, which can be dedicated to the use of robust min-max-prototypes. Finally, we give suggestions for future research for GLVQ based on min-max-prototypes.

While high dimensionality and the selection of meaningful features is usually a burden in machine learning, it is even more so in the case of unsupervised learning and particularly in clustering. The presence of uninformative features, sometimes correlated, may bias significantly the results of distance-based methods such as k-means for instance. Since the seminal work of Witten et al. (2010), different versions of sparse k-means have been introduced, building on the idea of adding some penalty terms in the loss function and resulting into automatic feature selection and/or weighting. This paper investigates the connections between some of these methods, and particularly the differences induced by the choices of the penalty terms. It also focuses on the case of mixed data, and how they may be handled by the sparse k-means approaches. Eventually, it presents the algorithms and model selection tools made available through a recently implemented R package, vimpclust.