5,222 research outputs found
Robust Localized Multi-view Subspace Clustering
In multi-view clustering, different views may have different confidence
levels when learning a consensus representation. Existing methods usually
address this by assigning distinctive weights to different views. However, due
to noisy nature of real-world applications, the confidence levels of samples in
the same view may also vary. Thus considering a unified weight for a view may
lead to suboptimal solutions. In this paper, we propose a novel localized
multi-view subspace clustering model that considers the confidence levels of
both views and samples. By assigning weight to each sample under each view
properly, we can obtain a robust consensus representation via fusing the
noiseless structures among views and samples. We further develop a regularizer
on weight parameters based on the convex conjugacy theory, and samples weights
are determined in an adaptive manner. An efficient iterative algorithm is
developed with a convergence guarantee. Experimental results on four benchmarks
demonstrate the correctness and effectiveness of the proposed model.Comment: 7 page
Feature Concatenation Multi-view Subspace Clustering
Multi-view clustering aims to achieve more promising clustering results than
single-view clustering by exploring the multi-view information. Since statistic
properties of different views are diverse, even incompatible, few approaches
implement multi-view clustering based on the concatenated features directly.
However, feature concatenation is a natural way to combine multiple views. To
this end, this paper proposes a novel multi-view subspace clustering approach
dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC).
Specifically, by exploring the consensus information, multi-view data are
concatenated into a joint representation firstly, then, -norm is
integrated into the objective function to deal with the sample-specific and
cluster-specific corruptions of multiple views for benefiting the clustering
performance. Furthermore, by introducing graph Laplacians of multiple views, a
graph regularized FCMSC is also introduced to explore both the consensus
information and complementary information for clustering. It is noteworthy that
the obtained coefficient matrix is not derived by directly applying the
Low-Rank Representation (LRR) to the joint view representation simply. Finally,
an effective algorithm based on the Augmented Lagrangian Multiplier (ALM) is
designed to optimized the objective functions. Comprehensive experiments on six
real world datasets illustrate the superiority of the proposed methods over
several state-of-the-art approaches for multi-view clustering
Low-rank Kernel Learning for Graph-based Clustering
Constructing the adjacency graph is fundamental to graph-based clustering.
Graph learning in kernel space has shown impressive performance on a number of
benchmark data sets. However, its performance is largely determined by the
chosen kernel matrix. To address this issue, the previous multiple kernel
learning algorithm has been applied to learn an optimal kernel from a group of
predefined kernels. This approach might be sensitive to noise and limits the
representation ability of the consensus kernel. In contrast to existing
methods, we propose to learn a low-rank kernel matrix which exploits the
similarity nature of the kernel matrix and seeks an optimal kernel from the
neighborhood of candidate kernels. By formulating graph construction and kernel
learning in a unified framework, the graph and consensus kernel can be
iteratively enhanced by each other. Extensive experimental results validate the
efficacy of the proposed method
Guided Co-training for Large-Scale Multi-View Spectral Clustering
In many real-world applications, we have access to multiple views of the
data, each of which characterizes the data from a distinct aspect. Several
previous algorithms have demonstrated that one can achieve better clustering
accuracy by integrating information from all views appropriately than using
only an individual view. Owing to the effectiveness of spectral clustering,
many multi-view clustering methods are based on it. Unfortunately, they have
limited applicability to large-scale data due to the high computational
complexity of spectral clustering. In this work, we propose a novel multi-view
spectral clustering method for large-scale data. Our approach is structured
under the guided co-training scheme to fuse distinct views, and uses the
sampling technique to accelerate spectral clustering. More specifically, we
first select () landmark points and then approximate the
eigen-decomposition accordingly. The augmented view, which is essential to
guided co-training process, can then be quickly determined by our method. The
proposed algorithm scales linearly with the number of given data. Extensive
experiments have been performed and the results support the advantage of our
method for handling the large-scale multi-view situation
Convex Sparse Spectral Clustering: Single-view to Multi-view
Spectral Clustering (SC) is one of the most widely used methods for data
clustering. It first finds a low-dimensonal embedding of data by computing
the eigenvectors of the normalized Laplacian matrix, and then performs k-means
on to get the final clustering result. In this work, we observe that,
in the ideal case, should be block diagonal and thus sparse.
Therefore we propose the Sparse Spectral Clustering (SSC) method which extends
SC with sparse regularization on . To address the computational issue
of the nonconvex SSC model, we propose a novel convex relaxation of SSC based
on the convex hull of the fixed rank projection matrices. Then the convex SSC
model can be efficiently solved by the Alternating Direction Method of
\canyi{Multipliers} (ADMM). Furthermore, we propose the Pairwise Sparse
Spectral Clustering (PSSC) which extends SSC to boost the clustering
performance by using the multi-view information of data. Experimental
comparisons with several baselines on real-world datasets testify to the
efficacy of our proposed methods
Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering
Due to the existence of various views or representations in many real-world
data, multi-view learning has drawn much attention recently. Multi-view
spectral clustering methods based on similarity matrixes or graphs are pretty
popular. Generally, these algorithms learn informative graphs by directly
utilizing original data. However, in the real-world applications, original data
often contain noises and outliers that lead to unreliable graphs. In addition,
different views may have different contributions to data clustering. In this
paper, a novel Multiview Subspace Clustering method unifying Adaptive
neighbours and Metric learning (MSCAM), is proposed to address the above
problems. In this method, we use the subspace representations of different
views to adaptively learn a consensus similarity matrix, uncovering the
subspace structure and avoiding noisy nature of original data. For all views,
we also learn different Mahalanobis matrixes that parameterize the squared
distances and consider the contributions of different views. Further, we
constrain the graph constructed by the similarity matrix to have exact c (c is
the number of clusters) connected components. An iterative algorithm is
developed to solve this optimization problem. Moreover, experiments on a
synthetic dataset and different real-world datasets demonstrate the
effectiveness of MSCAM.Comment: 9 page
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its
aim is to leverage useful information contained in multiple related tasks to
help improve the generalization performance of all the tasks. In this paper, we
give a survey for MTL. First, we classify different MTL algorithms into several
categories, including feature learning approach, low-rank approach, task
clustering approach, task relation learning approach, and decomposition
approach, and then discuss the characteristics of each approach. In order to
improve the performance of learning tasks further, MTL can be combined with
other learning paradigms including semi-supervised learning, active learning,
unsupervised learning, reinforcement learning, multi-view learning and
graphical models. When the number of tasks is large or the data dimensionality
is high, batch MTL models are difficult to handle this situation and online,
parallel and distributed MTL models as well as dimensionality reduction and
feature hashing are reviewed to reveal their computational and storage
advantages. Many real-world applications use MTL to boost their performance and
we review representative works. Finally, we present theoretical analyses and
discuss several future directions for MTL
Deep Multimodal Subspace Clustering Networks
We present convolutional neural network (CNN) based approaches for
unsupervised multimodal subspace clustering. The proposed framework consists of
three main stages - multimodal encoder, self-expressive layer, and multimodal
decoder. The encoder takes multimodal data as input and fuses them to a latent
space representation. The self-expressive layer is responsible for enforcing
the self-expressiveness property and acquiring an affinity matrix corresponding
to the data points. The decoder reconstructs the original input data. The
network uses the distance between the decoder's reconstruction and the original
input in its training. We investigate early, late and intermediate fusion
techniques and propose three different encoders corresponding to them for
spatial fusion. The self-expressive layers and multimodal decoders are
essentially the same for different spatial fusion-based approaches. In addition
to various spatial fusion-based methods, an affinity fusion-based network is
also proposed in which the self-expressive layer corresponding to different
modalities is enforced to be the same. Extensive experiments on three datasets
show that the proposed methods significantly outperform the state-of-the-art
multimodal subspace clustering methods
A Survey on Multi-View Clustering
With advances in information acquisition technologies, multi-view data become
ubiquitous. Multi-view learning has thus become more and more popular in
machine learning and data mining fields. Multi-view unsupervised or
semi-supervised learning, such as co-training, co-regularization has gained
considerable attention. Although recently, multi-view clustering (MVC) methods
have been developed rapidly, there has not been a survey to summarize and
analyze the current progress. Therefore, this paper reviews the common
strategies for combining multiple views of data and based on this summary we
propose a novel taxonomy of the MVC approaches. We further discuss the
relationships between MVC and multi-view representation, ensemble clustering,
multi-task clustering, multi-view supervised and semi-supervised learning.
Several representative real-world applications are elaborated. To promote
future development of MVC, we envision several open problems that may require
further investigation and thorough examination.Comment: 17 pages, 4 figure
Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data
In this paper, we consider the problem of subspace clustering in presence of
contiguous noise, occlusion and disguise. We argue that self-expressive
representation of data in current state-of-the-art approaches is severely
sensitive to occlusions and complex real-world noises. To alleviate this
problem, we propose a hierarchical framework that brings robustness of local
patches-based representations and discriminant property of global
representations together. This approach consists of 1) a top-down stage, in
which the input data is subject to repeated division to smaller patches and 2)
a bottom-up stage, in which the low rank embedding of local patches in field of
view of a corresponding patch in upper level are merged on a Grassmann
manifold. This summarized information provides two key information for the
corresponding patch on the upper level: cannot-links and recommended-links.
This information is employed for computing a self-expressive representation of
each patch at upper levels using a weighted sparse group lasso optimization
problem. Numerical results on several real data sets confirm the efficiency of
our approach
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