Nearly-Tight and Oblivious Algorithms for Explainable Clustering
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The problem of clustering in urban traffic networks has been mainly studied in static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be ...
The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show l ...
EDM2016
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look at Aby Warburg’s concept of Pathosformel, the repeatable formula for the expression of emotion, through the depiction of human pose in art. Using crowdsourcing, we annotate 2D human pose in one-third of the panels of Warburg’s atlas of art, and perfor ...
The aim of the domain-adaptation task for speaker verification is to exploit unlabelled target domain data by using the labelled source domain data effectively. The i-vector based Probabilistic Linear Dis- criminant Analysis (PLDA) framework approaches thi ...
The aim of the domain-adaptation task for speaker verification is to exploit unlabelled target domain data by using the labelled source domain data effectively. The i-vector based Probabilistic Linear Dis- criminant Analysis (PLDA) framework approaches thi ...
Non-negative matrix factorization (NMF) based sound source separation involves two phases: First, the signal spectrum is decomposed into components which, in a second step, are clustered in order to obtain estimates of the source signal spectra. The major ...
The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative cluster ...
In this paper, we discuss the adaptation of our decentralized place recognition method described in [1] to fullimage descriptors. As we had shown, the key to making a scalable decentralized visual place recognition lies in exploting deterministic key assig ...
In this paper we address the problem of detecting and localizing objects that can be both seen and heard, e.g., people. This may be solved within the framework of data clustering. We propose a new multimodal clustering algorithm based on a Gaussian mixture ...
We propose to apply statistical clustering algorithms on a three-dimensional profile of red blood cells (RBCs) obtained through digital holographic microscopy (DHM). We show that two classes of RBCs stored for 14 and 38 days can be effectively classified. ...
