Author identification is a hot topic, especially in the Internet age. Following our previous work in which we proposed a novel approach to this problem, based on relational representations that take into account the structure of sentences, here we present a tool that computes and visualizes a numerical and graphical characterization of the authors/texts based on several linguistic features. Thi...

Music key, a high level feature of musical audio, is an effective tool for structural analysis of musical works. This paper presents a novel unsupervised approach for clustering music recordings by their keys. Based on chroma-based features extracted from acoustic signals, an inter-recording distance metric which characterizes diversity of pitch distribution together with harmonic center of mus...

Topology and machine learning are two actively researched topics not only in condensed matter physics, but also data science. Here, we propose the use of topological analysis unsupervised phase diagrams. This is possible because quantum distance can capture shape space formed by Bloch wavefunctions as sweep over Brillouin zone. Therefore, if minimize volume wavefunction through a continuous def...

We propose an unsupervised method for learning a generic and efficient shape encoding network different analysis tasks. Our key idea is to jointly encode learn point features from unlabeled 3D clouds. For this purpose, we adapt HRNet octree-based convolutional neural networks with fused multiresolution subnetworks design simple-yet-efficient Multiresolution Instance Discrimination (MID) loss th...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

Within this paper we present a framework for discovering patterns in time-series by unsupervised feature selection and unsupervised, self-organised clustering. The proposed unsupervised feature selection algorithm is determining the feature relevance for a variety of transformations to select a set of features to build the feature space. We propose to take the phase space as basis and extend it...

Online debates sparkle argumentative discussions from which generally accepted arguments often emerge. We consider the task of unsupervised identification of prominent argument in online debates. As a first step, in this paper we perform a cluster analysis using semantic textual similarity to detect similar arguments. We perform a preliminary cluster evaluation and error analysis based on clust...