1,921 research outputs found
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify
clusters based on their associations with subspaces in high dimensional spaces.
SC can be classified into hard subspace clustering (HSC) and soft subspace
clustering (SSC). While HSC algorithms have been extensively studied and well
accepted by the scientific community, SSC algorithms are relatively new but
gaining more attention in recent years due to better adaptability. In the
paper, a comprehensive survey on existing SSC algorithms and the recent
development are presented. The SSC algorithms are classified systematically
into three main categories, namely, conventional SSC (CSSC), independent SSC
(ISSC) and extended SSC (XSSC). The characteristics of these algorithms are
highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
In this paper, we propose GPSP, a novel Graph Partition and Space Projection
based approach, to learn the representation of a heterogeneous network that
consists of multiple types of nodes and links. Concretely, we first partition
the heterogeneous network into homogeneous and bipartite subnetworks. Then, the
projective relations hidden in bipartite subnetworks are extracted by learning
the projective embedding vectors. Finally, we concatenate the projective
vectors from bipartite subnetworks with the ones learned from homogeneous
subnetworks to form the final representation of the heterogeneous network.
Extensive experiments are conducted on a real-life dataset. The results
demonstrate that GPSP outperforms the state-of-the-art baselines in two key
network mining tasks: node classification and clustering.Comment: WWW 2018 Poste
Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods
BiETech : Bicluster Ensemble Techniques
Various biclustering algorithms have emerged now a days that try to deliver good biclusters from gene expression data which satisfy a particular objective function. Users are lost in finding the best out of these algorithms. Ensemble techniques come to rescue of these users by aggregating all the solutions and providing a single solution which is more robust and stable than its constituent solutions. In this paper, we present two different ensemble techniques for biclustering solutions. We have used classifiers in one approach and the other approach uses the concept of metaclustering for forming the consensus. Experiments in this research are performed on synthetic and real gene expression datasets as biologists are interested in finding meaningful patterns in expression of genes. The experiments show that both the approaches proposed in the paper show improvement over the input solutions as well as the existing bicluster ensemble techniques
Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking
Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática
Equation-free analysis of a dynamically evolving multigraph
In order to illustrate the adaptation of traditional continuum numerical
techniques to the study of complex network systems, we use the equation-free
framework to analyze a dynamically evolving multigraph. This approach is based
on coupling short intervals of direct dynamic network simulation with
appropriately-defined lifting and restriction operators, mapping the detailed
network description to suitable macroscopic (coarse-grained) variables and
back. This enables the acceleration of direct simulations through Coarse
Projective Integration (CPI), as well as the identification of coarse
stationary states via a Newton-GMRES method. We also demonstrate the use of
data-mining, both linear (principal component analysis, PCA) and nonlinear
(diffusion maps, DMAPS) to determine good macroscopic variables (observables)
through which one can coarse-grain the model. These results suggest methods for
decreasing simulation times of dynamic real-world systems such as
epidemiological network models. Additionally, the data-mining techniques could
be applied to a diverse class of problems to search for a succint,
low-dimensional description of the system in a small number of variables
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