150,820 research outputs found
A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective
Multi-view clustering (MVC) is a popular technique for improving clustering
performance using various data sources. However, existing methods primarily
focus on acquiring consistent information while often neglecting the issue of
redundancy across multiple views. This study presents a new approach called
Sufficient Multi-View Clustering (SUMVC) that examines the multi-view
clustering framework from an information-theoretic standpoint. Our proposed
method consists of two parts. Firstly, we develop a simple and reliable
multi-view clustering method SCMVC (simple consistent multi-view clustering)
that employs variational analysis to generate consistent information. Secondly,
we propose a sufficient representation lower bound to enhance consistent
information and minimise unnecessary information among views. The proposed
SUMVC method offers a promising solution to the problem of multi-view
clustering and provides a new perspective for analyzing multi-view data.
To verify the effectiveness of our model, we conducted a theoretical analysis
based on the Bayes Error Rate, and experiments on multiple multi-view datasets
demonstrate the superior performance of SUMVC
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Benefiting from the strong view-consistent information mining capacity,
multi-view contrastive clustering has attracted plenty of attention in recent
years. However, we observe the following drawback, which limits the clustering
performance from further improvement. The existing multi-view models mainly
focus on the consistency of the same samples in different views while ignoring
the circumstance of similar but different samples in cross-view scenarios. To
solve this problem, we propose a novel Dual contrastive calibration network for
Multi-View Clustering (DealMVC). Specifically, we first design a fusion
mechanism to obtain a global cross-view feature. Then, a global contrastive
calibration loss is proposed by aligning the view feature similarity graph and
the high-confidence pseudo-label graph. Moreover, to utilize the diversity of
multi-view information, we propose a local contrastive calibration loss to
constrain the consistency of pair-wise view features. The feature structure is
regularized by reliable class information, thus guaranteeing similar samples
have similar features in different views. During the training procedure, the
interacted cross-view feature is jointly optimized at both local and global
levels. In comparison with other state-of-the-art approaches, the comprehensive
experimental results obtained from eight benchmark datasets provide substantial
validation of the effectiveness and superiority of our algorithm. We release
the code of DealMVC at https://github.com/xihongyang1999/DealMVC on GitHub
Pedestrian detection in uncontrolled environments using stereo and biometric information
A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios
Radio Galaxy Detection in the Visibility Domain
We explore a new Bayesian method of detecting galaxies from radio
interferometric data of the faint sky. Working in the Fourier domain, we fit a
single, parameterised galaxy model to simulated visibility data of star-forming
galaxies. The resulting multimodal posterior distribution is then sampled using
a multimodal nested sampling algorithm such as MultiNest. For each galaxy, we
construct parameter estimates for the position, flux, scale-length and
ellipticities from the posterior samples. We first test our approach on
simulated SKA1-MID visibility data of up to 100 galaxies in the field of view,
considering a typical weak lensing survey regime (SNR ) where 98% of
the input galaxies are detected with no spurious source detections. We then
explore the low SNR regime, finding our approach reliable in galaxy detection
and providing in particular high accuracy in positional estimates down to SNR
. The presented method does not require transformation of visibilities
to the image domain, and requires no prior knowledge of the number of galaxies
in the field of view, thus could become a useful tool for constructing accurate
radio galaxy catalogs in the future.Comment: 11 pages, 11 figures. Accepted for publication in MNRA
From Data Topology to a Modular Classifier
This article describes an approach to designing a distributed and modular
neural classifier. This approach introduces a new hierarchical clustering that
enables one to determine reliable regions in the representation space by
exploiting supervised information. A multilayer perceptron is then associated
with each of these detected clusters and charged with recognizing elements of
the associated cluster while rejecting all others. The obtained global
classifier is comprised of a set of cooperating neural networks and completed
by a K-nearest neighbor classifier charged with treating elements rejected by
all the neural networks. Experimental results for the handwritten digit
recognition problem and comparison with neural and statistical nonmodular
classifiers are given
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