1,392 research outputs found
Evidential relational clustering using medoids
In real clustering applications, proximity data, in which only pairwise
similarities or dissimilarities are known, is more general than object data, in
which each pattern is described explicitly by a list of attributes.
Medoid-based clustering algorithms, which assume the prototypes of classes are
objects, are of great value for partitioning relational data sets. In this
paper a new prototype-based clustering method, named Evidential C-Medoids
(ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical
framework of belief functions is proposed. In ECMdd, medoids are utilized as
the prototypes to represent the detected classes, including specific classes
and imprecise classes. Specific classes are for the data which are distinctly
far from the prototypes of other classes, while imprecise classes accept the
objects that may be close to the prototypes of more than one class. This soft
decision mechanism could make the clustering results more cautious and reduce
the misclassification rates. Experiments in synthetic and real data sets are
used to illustrate the performance of ECMdd. The results show that ECMdd could
capture well the uncertainty in the internal data structure. Moreover, it is
more robust to the initializations compared with FCMdd.Comment: in The 18th International Conference on Information Fusion, July
2015, Washington, DC, USA , Jul 2015, Washington, United State
Median evidential c-means algorithm and its application to community detection
Median clustering is of great value for partitioning relational data. In this
paper, a new prototype-based clustering method, called Median Evidential
C-Means (MECM), which is an extension of median c-means and median fuzzy
c-means on the theoretical framework of belief functions is proposed. The
median variant relaxes the restriction of a metric space embedding for the
objects but constrains the prototypes to be in the original data set. Due to
these properties, MECM could be applied to graph clustering problems. A
community detection scheme for social networks based on MECM is investigated
and the obtained credal partitions of graphs, which are more refined than crisp
and fuzzy ones, enable us to have a better understanding of the graph
structures. An initial prototype-selection scheme based on evidential
semi-centrality is presented to avoid local premature convergence and an
evidential modularity function is defined to choose the optimal number of
communities. Finally, experiments in synthetic and real data sets illustrate
the performance of MECM and show its difference to other methods
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
The Gaussian mixture model (GMM) provides a convenient yet principled
framework for clustering, with properties suitable for statistical inference.
In this paper, we propose a new model-based clustering algorithm, called EGMM
(evidential GMM), in the theoretical framework of belief functions to better
characterize cluster-membership uncertainty. With a mass function representing
the cluster membership of each object, the evidential Gaussian mixture
distribution composed of the components over the powerset of the desired
clusters is proposed to model the entire dataset. The parameters in EGMM are
estimated by a specially designed Expectation-Maximization (EM) algorithm. A
validity index allowing automatic determination of the proper number of
clusters is also provided. The proposed EGMM is as convenient as the classical
GMM, but can generate a more informative evidential partition for the
considered dataset. Experiments with synthetic and real datasets demonstrate
the good performance of the proposed method as compared with some other
prototype-based and model-based clustering techniques
Uncertainty-aware Panoptic Segmentation
Reliable scene understanding is indispensable for modern autonomous systems.
Current learning-based methods typically try to maximize their performance
based on segmentation metrics that only consider the quality of the
segmentation. However, for the safe operation of a system in the real world it
is crucial to consider the uncertainty in the prediction as well. In this work,
we introduce the novel task of uncertainty-aware panoptic segmentation, which
aims to predict per-pixel semantic and instance segmentations, together with
per-pixel uncertainty estimates. We define two novel metrics to facilitate its
quantitative analysis, the uncertainty-aware Panoptic Quality (uPQ) and the
panoptic Expected Calibration Error (pECE). We further propose the novel
top-down Evidential Panoptic Segmentation Network (EvPSNet) to solve this task.
Our architecture employs a simple yet effective panoptic fusion module that
leverages the predicted uncertainties. Furthermore, we provide several strong
baselines combining state-of-the-art panoptic segmentation networks with
sampling-free uncertainty estimation techniques. Extensive evaluations show
that our EvPSNet achieves the new state-of-the-art for the standard Panoptic
Quality (PQ), as well as for our uncertainty-aware panoptic metrics. We make
the code available at: \url{https://github.com/kshitij3112/EvPSNet
Belief Functions: Theory and Algorithms
The subject of this thesis is belief function theory and its application in different contexts. Belief function theory can be interpreted as a generalization of Bayesian probability theory and makes it possible to distinguish between different types of uncertainty. In this thesis, applications of belief function theory are explored both on a theoretical and on an algorithmic level. The problem of exponential complexity associated with belief function inference is addressed in this thesis by showing how efficient algorithms can be developed based on Monte-Carlo approximations and exploitation of independence. The effectiveness of these algorithms is demonstrated in applications to particle filtering, simultaneous localization and mapping, and active classification
Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review
Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory
Representation recovers information
Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation-use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is *necessary* for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation-use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing
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