211 research outputs found
A Computational Model of the Short-Cut Rule for 2D Shape Decomposition
We propose a new 2D shape decomposition method based on the short-cut rule.
The short-cut rule originates from cognition research, and states that the
human visual system prefers to partition an object into parts using the
shortest possible cuts. We propose and implement a computational model for the
short-cut rule and apply it to the problem of shape decomposition. The model we
proposed generates a set of cut hypotheses passing through the points on the
silhouette which represent the negative minima of curvature. We then show that
most part-cut hypotheses can be eliminated by analysis of local properties of
each. Finally, the remaining hypotheses are evaluated in ascending length
order, which guarantees that of any pair of conflicting cuts only the shortest
will be accepted. We demonstrate that, compared with state-of-the-art shape
decomposition methods, the proposed approach achieves decomposition results
which better correspond to human intuition as revealed in psychological
experiments.Comment: 11 page
Late Fusion Multi-view Clustering via Global and Local Alignment Maximization
Multi-view clustering (MVC) optimally integrates complementary information
from different views to improve clustering performance. Although demonstrating
promising performance in various applications, most of existing approaches
directly fuse multiple pre-specified similarities to learn an optimal
similarity matrix for clustering, which could cause over-complicated
optimization and intensive computational cost. In this paper, we propose late
fusion MVC via alignment maximization to address these issues. To do so, we
first reveal the theoretical connection of existing k-means clustering and the
alignment between base partitions and the consensus one. Based on this
observation, we propose a simple but effective multi-view algorithm termed
LF-MVC-GAM. It optimally fuses multiple source information in partition level
from each individual view, and maximally aligns the consensus partition with
these weighted base ones. Such an alignment is beneficial to integrate
partition level information and significantly reduce the computational
complexity by sufficiently simplifying the optimization procedure. We then
design another variant, LF-MVC-LAM to further improve the clustering
performance by preserving the local intrinsic structure among multiple
partition spaces. After that, we develop two three-step iterative algorithms to
solve the resultant optimization problems with theoretically guaranteed
convergence. Further, we provide the generalization error bound analysis of the
proposed algorithms. Extensive experiments on eighteen multi-view benchmark
datasets demonstrate the effectiveness and efficiency of the proposed
LF-MVC-GAM and LF-MVC-LAM, ranging from small to large-scale data items. The
codes of the proposed algorithms are publicly available at
https://github.com/wangsiwei2010/latefusionalignment
Consensus Kernel K
Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectively handle the view’s incompleteness. Recently, one pioneering work was proposed that handled this issue by integrating multiview clustering and imputation into a unified learning framework. While its framework is elegant, we observe that it overlooks the consistency between views, which leads to a reduction in the clustering performance. In order to address this issue, we propose a new unified learning method for incomplete multiview clustering, which simultaneously imputes the incomplete views and learns a consistent clustering result with explicit modeling of between-view consistency. More specifically, the similarity between each view’s clustering result and the consistent clustering result is measured. The consistency between views is then modeled using the sum of these similarities. Incomplete views are imputed to achieve an optimal clustering result in each view, while maintaining between-view consistency. Extensive comparisons with state-of-the-art methods on both synthetic and real-world incomplete multiview datasets validate the superiority of the proposed method
Outlier Detection Ensemble with Embedded Feature Selection
Feature selection places an important role in improving the performance of
outlier detection, especially for noisy data. Existing methods usually perform
feature selection and outlier scoring separately, which would select feature
subsets that may not optimally serve for outlier detection, leading to
unsatisfying performance. In this paper, we propose an outlier detection
ensemble framework with embedded feature selection (ODEFS), to address this
issue. Specifically, for each random sub-sampling based learning component,
ODEFS unifies feature selection and outlier detection into a pairwise ranking
formulation to learn feature subsets that are tailored for the outlier
detection method. Moreover, we adopt the thresholded self-paced learning to
simultaneously optimize feature selection and example selection, which is
helpful to improve the reliability of the training set. After that, we design
an alternate algorithm with proved convergence to solve the resultant
optimization problem. In addition, we analyze the generalization error bound of
the proposed framework, which provides theoretical guarantee on the method and
insightful practical guidance. Comprehensive experimental results on 12
real-world datasets from diverse domains validate the superiority of the
proposed ODEFS.Comment: 10pages, AAAI202
Parametric Extension of the Most Preferred OWA Operator and Its Application in Search Engine's Rank
Most preferred ordered weighted average (MP-OWA) operator is a new kind of neat (dynamic weight) OWA operator in the aggregation operator families. It considers the preferences of all alternatives across the criteria and provides unique aggregation characteristics in decision making. In this paper, we propose the parametric form of the MP-OWA operator to deal with the uncertainty preference information, which includes MP-OWA operator as its special case, and it also includes the most commonly used maximum, minimum, and average aggregation operators. A special form of parametric MP-OWA operator with power function is proposed. Some properties of the parametric MP-OWA operator are provided and the advantages of them in decision making problems are summarized. The new proposed parametric MP-OWA operator can grasp the subtle preference information of the decision maker according to the application context through multiple aggregation results. They are applied to rank search engines considering the relevance of the retrieved queries. An search engine ranking example illustrates the application of parametric MP-OWA operator in various decision situations
Decoupled Diffusion Models with Explicit Transition Probability
Recent diffusion probabilistic models (DPMs) have shown remarkable abilities
of generated content, however, they often suffer from complex forward
processes, resulting in inefficient solutions for the reversed process and
prolonged sampling times. In this paper, we aim to address the aforementioned
challenges by focusing on the diffusion process itself that we propose to
decouple the intricate diffusion process into two comparatively simpler process
to improve the generative efficacy and speed. In particular, we present a novel
diffusion paradigm named DDM (Decoupled Diffusion Models) based on the Ito
diffusion process, in which the image distribution is approximated by an
explicit transition probability while the noise path is controlled by the
standard Wiener process. We find that decoupling the diffusion process reduces
the learning difficulty and the explicit transition probability improves the
generative speed significantly. We prove a new training objective for DPM,
which enables the model to learn to predict the noise and image components
separately. Moreover, given the novel forward diffusion equation, we derive the
reverse denoising formula of DDM that naturally supports fewer steps of
generation without ordinary differential equation (ODE) based accelerators. Our
experiments demonstrate that DDM outperforms previous DPMs by a large margin in
fewer function evaluations setting and gets comparable performances in long
function evaluations setting. We also show that our framework can be applied to
image-conditioned generation and high-resolution image synthesis, and that it
can generate high-quality images with only 10 function evaluations
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