2,801 research outputs found
EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) methods have proved to be powerful
across a wide range of real-world clustering applications. Integrating multiple
types of measurements for the same objects/subjects allows us to gain a deeper
understanding of the data and refine the clustering. We have developed a novel
Graph-reguarized multiview NMF-based method for data integration called
EquiNMF. The parameters for our method are set in a completely automated
data-specific unsupervised fashion, a highly desirable property in real-world
applications. We performed extensive and comprehensive experiments on multiview
imaging data. We show that EquiNMF consistently outperforms other single-view
NMF methods used on concatenated data and multi-view NMF methods with different
types of regularizations
Graph Multiview Canonical Correlation Analysis
Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional
representations encountered with multiview data of shared entities (a.k.a.
common sources). However, existing MCCA approaches do not exploit the geometry
of the common sources, which may be available \emph{a priori}, or can be
constructed using certain domain knowledge. This prior information about the
common sources can be encoded by a graph, and be invoked as a regularizer to
enrich the maximum variance MCCA framework. In this context, the present
paper's novel graph-regularized (G) MCCA approach minimizes the distance
between the wanted canonical variables and the common low-dimensional
representations, while accounting for graph-induced knowledge of the common
sources. Relying on a function capturing the extent low-dimensional
representations of the multiple views are similar, a generalization bound of
GMCCA is established based on Rademacher's complexity. Tailored for setups
where the number of data pairs is smaller than the data vector dimensions, a
graph-regularized dual MCCA approach is also developed. To further deal with
nonlinearities present in the data, graph-regularized kernel MCCA variants are
put forward too. Interestingly, solutions of the graph-regularized linear,
dual, and kernel MCCA, are all provided in terms of generalized eigenvalue
decomposition. Several corroborating numerical tests using real datasets are
provided to showcase the merits of the graph-regularized MCCA variants relative
to several competing alternatives including MCCA, Laplacian-regularized MCCA,
and (graph-regularized) PCA
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
Analysis of multiview legislative networks with structured matrix factorization: Does Twitter influence translate to the real world?
The rise of social media platforms has fundamentally altered the public
discourse by providing easy to use and ubiquitous forums for the exchange of
ideas and opinions. Elected officials often use such platforms for
communication with the broader public to disseminate information and engage
with their constituencies and other public officials. In this work, we
investigate whether Twitter conversations between legislators reveal their
real-world position and influence by analyzing multiple Twitter networks that
feature different types of link relations between the Members of Parliament
(MPs) in the United Kingdom and an identical data set for politicians within
Ireland. We develop and apply a matrix factorization technique that allows the
analyst to emphasize nodes with contextual local network structures by
specifying network statistics that guide the factorization solution. Leveraging
only link relation data, we find that important politicians in Twitter networks
are associated with real-world leadership positions, and that rankings from the
proposed method are correlated with the number of future media headlines.Comment: Published at http://dx.doi.org/10.1214/15-AOAS858 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Kernelized Multiview Projection
Conventional vision algorithms adopt a single type of feature or a simple
concatenation of multiple features, which is always represented in a
high-dimensional space. In this paper, we propose a novel unsupervised spectral
embedding algorithm called Kernelized Multiview Projection (KMP) to better fuse
and embed different feature representations. Computing the kernel matrices from
different features/views, KMP can encode them with the corresponding weights to
achieve a low-dimensional and semantically meaningful subspace where the
distribution of each view is sufficiently smooth and discriminative. More
crucially, KMP is linear for the reproducing kernel Hilbert space (RKHS) and
solves the out-of-sample problem, which allows it to be competent for various
practical applications. Extensive experiments on three popular image datasets
demonstrate the effectiveness of our multiview embedding algorithm
Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques
We propose a novel unsupervised approach for sleep dynamics exploration and
automatic annotation by combining modern harmonic analysis tools. Specifically,
we apply diffusion-based algorithms, diffusion map (DM) and alternating
diffusion (AD) algorithms, to reconstruct the intrinsic geometry of sleep
dynamics by reorganizing the spectral information of an electroencephalogram
(EEG) extracted from a nonlinear-type time frequency analysis tool, the
synchrosqueezing transform (SST). The visualization is achieved by the
nonlinear dimension reduction properties of DM and AD. Moreover, the
reconstructed nonlinear geometric structure of the sleep dynamics allows us to
achieve the automatic annotation purpose. The hidden Markov model is trained to
predict the sleep stage. The prediction performance is validated on a publicly
available benchmark database, Physionet Sleep-EDF [extended] SC* and ST*, with
the leave-one-subject-out cross validation. The overall accuracy and macro F1
achieve 82:57% and 76% in Sleep-EDF SC* and 77.01% and 71:53% in Sleep-EDF ST*,
which is compatible with the state-of-the-art results by supervised
learning-based algorithms. The results suggest the potential of the proposed
algorithm for clinical applications.Comment: 41 pages, 21 figures. arXiv admin note: text overlap with
arXiv:1803.0171
Depth Sequence Coding with Hierarchical Partitioning and Spatial-domain Quantisation
Depth coding in 3D-HEVC for the multiview video plus depth (MVD) architecture
(i) deforms object shapes due to block-level edge-approximation; (ii) misses an
opportunity for high compressibility at near-lossless quality by failing to
exploit strong homogeneity (clustering tendency) in depth syntax, motion vector
components, and residuals at frame-level; and (iii) restricts interactivity and
limits responsiveness of independent use of depth information for "non-viewing"
applications due to texture-depth coding dependency. This paper presents a
standalone depth sequence coder, which operates in the lossless to
near-lossless quality range while compressing depth data superior to lossy
3D-HEVC. It preserves edges implicitly by limiting quantisation to the
spatial-domain and exploits clustering tendency efficiently at frame-level with
a novel binary tree based decomposition (BTBD) technique. For mono-view coding
of standard MVD test sequences, on average, (i) lossless BTBD achieved compression-ratio and coding gain against the pseudo-lossless
3D-HEVC, using the lowest quantisation parameter , and (ii)
near-lossless BTBD achieved and dB Bj{\o}ntegaard delta
bitrate (BD-BR) and distortion (BD-PSNR), respectively, against 3D-HEVC. In
view-synthesis applications, decoded depth maps from BTBD rendered superior
quality synthetic-views, compared to 3D-HEVC, with depth BD-BR and
dB synthetic-texture BD-PSNR on average.Comment: Submitted to IEEE Transactions on Image Processing. 13 pages, 5
figures, and 5 table
K-means clustering for efficient and robust registration of multi-view point sets
Generally, there are three main factors that determine the practical
usability of registration, i.e., accuracy, robustness, and efficiency. In
real-time applications, efficiency and robustness are more important. To
promote these two abilities, we cast the multi-view registration into a
clustering task. All the centroids are uniformly sampled from the initially
aligned point sets involved in the multi-view registration, which makes it
rather efficient and effective for the clustering. Then, each point is assigned
to a single cluster and each cluster centroid is updated accordingly.
Subsequently, the shape comprised by all cluster centroids is used to
sequentially estimate the rigid transformation for each point set. For accuracy
and stability, clustering and transformation estimation are alternately and
iteratively applied to all point sets. We tested our proposed approach on
several benchmark datasets and compared it with state-of-the-art approaches.
Experimental results validate its efficiency and robustness for the
registration of multi-view point sets
A Survey on Semi-Supervised Learning Techniques
Semisupervised learning is a learning standard which deals with the study of
how computers and natural systems such as human beings acquire knowledge in the
presence of both labeled and unlabeled data. Semisupervised learning based
methods are preferred when compared to the supervised and unsupervised learning
because of the improved performance shown by the semisupervised approaches in
the presence of large volumes of data. Labels are very hard to attain while
unlabeled data are surplus, therefore semisupervised learning is a noble
indication to shrink human labor and improve accuracy. There has been a large
spectrum of ideas on semisupervised learning. In this paper we bring out some
of the key approaches for semisupervised learning.Comment: 5 Pages, 3 figures, Published with International Journal of Computer
Trends and Technology (IJCTT
Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization
Multi-view data clustering attracts more attention than their single view
counterparts due to the fact that leveraging multiple independent and
complementary information from multi-view feature spaces outperforms the single
one. Multi-view Spectral Clustering aims at yielding the data partition
agreement over their local manifold structures by seeking
eigenvalue-eigenvector decompositions. However, as we observed, such classical
paradigm still suffers from (1) overlooking the flexible local manifold
structure, caused by (2) enforcing the low-rank data correlation agreement
among all views; worse still, (3) LRR is not intuitively flexible to capture
the latent data clustering structures. In this paper, we present the structured
LRR by factorizing into the latent low-dimensional data-cluster
representations, which characterize the data clustering structure for each
view. Upon such representation, (b) the laplacian regularizer is imposed to be
capable of preserving the flexible local manifold structure for each view. (c)
We present an iterative multi-view agreement strategy by minimizing the
divergence objective among all factorized latent data-cluster representations
during each iteration of optimization process, where such latent representation
from each view serves to regulate those from other views, such intuitive
process iteratively coordinates all views to be agreeable. (d) We remark that
such data-cluster representation can flexibly encode the data clustering
structure from any view with adaptive input cluster number. To this end, (e) a
novel non-convex objective function is proposed via the efficient alternating
minimization strategy. The complexity analysis are also presented. The
extensive experiments conducted against the real-world multi-view datasets
demonstrate the superiority over state-of-the-arts.Comment: Accepted to appear at IEEE Trans on Neural Networks and Learning
System
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