201,998 research outputs found
On multi-view learning with additive models
In many scientific settings data can be naturally partitioned into variable
groupings called views. Common examples include environmental (1st view) and
genetic information (2nd view) in ecological applications, chemical (1st view)
and biological (2nd view) data in drug discovery. Multi-view data also occur in
text analysis and proteomics applications where one view consists of a graph
with observations as the vertices and a weighted measure of pairwise similarity
between observations as the edges. Further, in several of these applications
the observations can be partitioned into two sets, one where the response is
observed (labeled) and the other where the response is not (unlabeled). The
problem for simultaneously addressing viewed data and incorporating unlabeled
observations in training is referred to as multi-view transductive learning. In
this work we introduce and study a comprehensive generalized fixed point
additive modeling framework for multi-view transductive learning, where any
view is represented by a linear smoother. The problem of view selection is
discussed using a generalized Akaike Information Criterion, which provides an
approach for testing the contribution of each view. An efficient implementation
is provided for fitting these models with both backfitting and local-scoring
type algorithms adjusted to semi-supervised graph-based learning. The proposed
technique is assessed on both synthetic and real data sets and is shown to be
competitive to state-of-the-art co-training and graph-based techniques.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS202 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Localized Sparse Incomplete Multi-view Clustering
Incomplete multi-view clustering, which aims to solve the clustering problem
on the incomplete multi-view data with partial view missing, has received more
and more attention in recent years. Although numerous methods have been
developed, most of the methods either cannot flexibly handle the incomplete
multi-view data with arbitrary missing views or do not consider the negative
factor of information imbalance among views. Moreover, some methods do not
fully explore the local structure of all incomplete views. To tackle these
problems, this paper proposes a simple but effective method, named localized
sparse incomplete multi-view clustering (LSIMVC). Different from the existing
methods, LSIMVC intends to learn a sparse and structured consensus latent
representation from the incomplete multi-view data by optimizing a sparse
regularized and novel graph embedded multi-view matrix factorization model.
Specifically, in such a novel model based on the matrix factorization, a l1
norm based sparse constraint is introduced to obtain the sparse low-dimensional
individual representations and the sparse consensus representation. Moreover, a
novel local graph embedding term is introduced to learn the structured
consensus representation. Different from the existing works, our local graph
embedding term aggregates the graph embedding task and consensus representation
learning task into a concise term. Furthermore, to reduce the imbalance factor
of incomplete multi-view learning, an adaptive weighted learning scheme is
introduced to LSIMVC. Finally, an efficient optimization strategy is given to
solve the optimization problem of our proposed model. Comprehensive
experimental results performed on six incomplete multi-view databases verify
that the performance of our LSIMVC is superior to the state-of-the-art IMC
approaches. The code is available in https://github.com/justsmart/LSIMVC.Comment: Published in IEEE Transactions on Multimedia (TMM). The code is
available at Github https://github.com/justsmart/LSIMV
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Anchor-based multi-view graph clustering (AMVGC) has received abundant
attention owing to its high efficiency and the capability to capture
complementary structural information across multiple views. Intuitively, a
high-quality anchor graph plays an essential role in the success of AMVGC.
However, the existing AMVGC methods only consider single-structure information,
i.e., local or global structure, which provides insufficient information for
the learning task. To be specific, the over-scattered global structure leads to
learned anchors failing to depict the cluster partition well. In contrast, the
local structure with an improper similarity measure results in potentially
inaccurate anchor assignment, ultimately leading to sub-optimal clustering
performance. To tackle the issue, we propose a novel anchor-based multi-view
graph clustering framework termed Efficient Multi-View Graph Clustering with
Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified
framework with a theoretical guarantee is designed to capture local and global
information. Besides, EMVGC-LG jointly optimizes anchor construction and graph
learning to enhance the clustering quality. In addition, EMVGC-LG inherits the
linear complexity of existing AMVGC methods respecting the sample number, which
is time-economical and scales well with the data size. Extensive experiments
demonstrate the effectiveness and efficiency of our proposed method.Comment: arXiv admin note: text overlap with arXiv:2308.1654
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning
Learning robot navigation strategies among pedestrian is crucial for domain
based applications. Combining perception, planning and prediction allows us to
model the interactions between robots and pedestrians, resulting in impressive
outcomes especially with recent approaches based on deep reinforcement learning
(RL). However, these works do not consider multi-robot scenarios. In this
paper, we present MultiSoc, a new method for learning multi-agent socially
aware navigation strategies using RL. Inspired by recent works on multi-agent
deep RL, our method leverages graph-based representation of agent interactions,
combining the positions and fields of view of entities (pedestrians and
agents). Each agent uses a model based on two Graph Neural Network combined
with attention mechanisms. First an edge-selector produces a sparse graph, then
a crowd coordinator applies node attention to produce a graph representing the
influence of each entity on the others. This is incorporated into a model-free
RL framework to learn multi-agent policies. We evaluate our approach on
simulation and provide a series of experiments in a set of various conditions
(number of agents / pedestrians). Empirical results show that our method learns
faster than social navigation deep RL mono-agent techniques, and enables
efficient multi-agent implicit coordination in challenging crowd navigation
with multiple heterogeneous humans. Furthermore, by incorporating customizable
meta-parameters, we can adjust the neighborhood density to take into account in
our navigation strategy
Contribution to Graph-based Multi-view Clustering: Algorithms and Applications
185 p.In this thesis, we study unsupervised learning, specifically, clustering methods for dividing data into meaningful groups. One major challenge is how to find an efficient algorithm with low computational complexity to deal with different types and sizes of datasets.For this purpose, we propose two approaches. The first approach is named "Multi-view Clustering via Kernelized Graph and Nonnegative Embedding" (MKGNE), and the second approach is called "Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding" (MVCGE). These two approaches jointly solve four tasks. They jointly estimate the unified similarity matrix over all views using the kernel tricks, the unified spectral projection of the data, the clusterindicator matrix, and the weight of each view without additional parameters. With these two approaches, there is no need for any postprocessing such as k-means clustering.In a further study, we propose a method named "Multi-view Spectral Clustering via Constrained Nonnegative Embedding" (CNESE). This method can overcome the drawbacks of the spectral clustering approaches, since they only provide a nonlinear projection of the data, on which an additional step of clustering is required. This can degrade the quality of the final clustering due to various factors such as the initialization process or outliers. Overcoming these drawbacks can be done by introducing a nonnegative embedding matrix which gives the final clustering assignment. In addition, some constraints are added to the targeted matrix to enhance the clustering performance.In accordance with the above methods, a new method called "Multi-view Spectral Clustering with a self-taught Robust Graph Learning" (MCSRGL) has been developed. Different from other approaches, this method integrates two main paradigms into the one-step multi-view clustering model. First, we construct an additional graph by using the cluster label space in addition to the graphs associated with the data space. Second, a smoothness constraint is exploited to constrain the cluster-label matrix and make it more consistent with the data views and the label view.Moreover, we propose two unified frameworks for multi-view clustering in Chapter 9. In these frameworks, we attempt to determine a view-based graphs, the consensus graph, the consensus spectral representation, and the soft clustering assignments. These methods retain the main advantages of the aforementioned methods and integrate the concepts of consensus and unified matrices. By using the unified matrices, we enforce the matrices of different views to be similar, and thus the problem of noise and inconsistency between different views will be reduced.Extensive experiments were conducted on several public datasets with different types and sizes, varying from face image datasets, to document datasets, handwritten datasets, and synthetics datasets. We provide several analyses of the proposed algorithms, including ablation studies, hyper-parameter sensitivity analyses, and computational costs. The experimental results show that the developed algorithms through this thesis are relevant and outperform several competing methods
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