3,913 research outputs found
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has been continuously evolving in
several areas like pattern recognition and information retrieval methods. It
factorizes a matrix into a product of 2 low-rank non-negative matrices that
will define parts-based, and linear representation of nonnegative data.
Recently, Graph regularized NMF (GrNMF) is proposed to find a compact
representation,which uncovers the hidden semantics and simultaneously respects
the intrinsic geometric structure. In GNMF, an affinity graph is constructed
from the original data space to encode the geometrical information. In this
paper, we propose a novel idea which engages a Multiple Kernel Learning
approach into refining the graph structure that reflects the factorization of
the matrix and the new data space. The GrNMF is improved by utilizing the graph
refined by the kernel learning, and then a novel kernel learning method is
introduced under the GrNMF framework. Our approach shows encouraging results of
the proposed algorithm in comparison to the state-of-the-art clustering
algorithms like NMF, GrNMF, SVD etc.Comment: This paper has been withdrawn by the author due to the terrible
writin
Emergent Leadership Detection Across Datasets
Automatic detection of emergent leaders in small groups from nonverbal
behaviour is a growing research topic in social signal processing but existing
methods were evaluated on single datasets -- an unrealistic assumption for
real-world applications in which systems are required to also work in settings
unseen at training time. It therefore remains unclear whether current methods
for emergent leadership detection generalise to similar but new settings and to
which extent. To overcome this limitation, we are the first to study a
cross-dataset evaluation setting for the emergent leadership detection task. We
provide evaluations for within- and cross-dataset prediction using two current
datasets (PAVIS and MPIIGroupInteraction), as well as an investigation on the
robustness of commonly used feature channels (visual focus of attention, body
pose, facial action units, speaking activity) and online prediction in the
cross-dataset setting. Our evaluations show that using pose and eye contact
based features, cross-dataset prediction is possible with an accuracy of 0.68,
as such providing another important piece of the puzzle towards emergent
leadership detection in the real world.Comment: 5 pages, 3 figure
A framework for developing motion-based games
Dissertação para obtenção do Grau de Mestre em
Engenharia InformáticaNowadays, whenever one intents to develop an application that allows interaction
through the use of more or less complex gestures, it is necessary to go through a long process. In this process, the gesture recognition system may not obtain high accuracy results, particularly among different users.
Since the total number of applications for mobile systems, like Android and iOS, is
close to a million and a half and is still increasing, it appears essential the development of a platform that abstracts developers from all the low-level gesture gathering and that streamlines the process of developing applications that make use of this kind of interaction, in a standardize way. In this case such was developed for the iOS system.
At the present time, given the existing environment issues, it is ideal to attract the attention, motivate and influence the greatest number of people into having more proenvironmental behaviors. Thus, as a proof of concept for the developed framework,
an educational game was created, using persuasive technology, to influence players’s
behaviors and attitudes in a pro-environmental way.
Therefore, having this idea as a basis, it was also developed a game that is presented
in a public ambient display and can be played by any participant close to the displaywho has a device with iOS mobile system
- …