100 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
Density propagation based adaptive multi-density clustering algorithm
This research was supported by the Science & Technology Development Foundation of Jilin Province (Grants Nos. 20160101259JC, 20180201045GX), the National Natural Science Foundation of China (Grants No. 61772227) and the Natural Science Foundation of Xinjiang Province (Grants No. 2015211C127). This resarch is also supported by the Engineering and Physical Sciences Research Council (EPSRC) funded project on New Industrial Systems: Manufacturing Immortality (EP/R020957/1).Peer reviewedPublisher PD
Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures
Single-cell sequencing technology can generate RNA-sequencing data at the single cell level, and one important single-cell RNA-sequencing data analysis method is to identify their cell types without supervised information. Clustering is an unsupervised approach that can help find new insights into biology especially for exploring the biological functions of specific cell type. However, it is challenging for traditional clustering methods to obtain high-quality cell type recognition results. In this research, we propose a novel Clustering method based on Matching Clusters Structures (MCSC) for identifying cell types among single-cell RNA-sequencing data. Firstly, MCSC obtains two different groups of clustering results from the same K-means algorithm because its initial centroids are randomly selected. Then, for one group, MCSC uses shared nearest neighbour information to calculate a label transition matrix, which denotes label transition probability between any two initial clusters. Each initial cluster may be reassigned if merging results after label transition satisfy a consensus function that maximizes structural matching degree of two different groups of clustering results. In essence, the MCSC may be interpreted as a label training process. We evaluate the proposed MCSC with five commonly used datasets and compare MCSC with several classical and state-of-the-art algorithms. The experimental results show that MCSC outperform other algorithms
LatinPSO : An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models
This research is supported by the National Natural Science Foundation of China (Grants Nos.61772227, 61572227), the Science & Technology Development Foundation of Jilin Province (Grants No. 20180201045GX), the Science Foundation of Education Department of Guangdong Province (Grants Nos. 2017KQNCX251, 2018XJCQSQ026) and the Social Science Foundation of Education Department of Jilin Province (Grants No. JJKH20181315SK). WP was supported by the 2015 Scottish Crucible award funded by Royal Society of Edinburgh.Peer reviewedPostprin
A systematic density-based clustering method using anchor points
National Research Foundation (NRF) Singapore under its AI Singapore Programme; Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Agein
Controlling electron motion with attosecond precision by shaped femtosecond intense laser pulse
We propose the scheme of temporal double-slit interferometer to precisely
measure the electric field of shaped intense femtosecond laser pulse directly,
and apply it to control the electron tunneling wave packets in attosecond
precision. By manipulating the spectra phase of the input femtosecond pulse in
frequency domain, one single pulse is split into two sub-pulses whose waveform
can be precisely controlled by adjusting the spectra phase. When the shaped
pulse interacts with atoms, the two sub-pulses are analogous to the Young's
double-slit in time domain. The interference pattern in the photoelectron
momentum distribution can be used to precisely retrieve the peak electric field
and the time delay between two sub-pulses. Based on the precise
characterization of the shaped pulse, we demonstrate that the sub-cycle
dynamics of electron can be controlled with attosecond precision. The above
scheme is proved to be feasible by both quantum-trajectory Monte Carlo
simulations and numerical solutions of three-dimensional time-dependent
Schr\"{o}dinger equation.Comment: 10 pages,4 figure
A Local Density Shape Context Algorithm for Point Pattern Matching in Three Dimensional Space
Three dimensional space point pattern matching technology shows significant usage in many scientific fields. It is a great challenge to match pairwise with rigid transformation in three dimensional space. In this paper, we propose an effect of Local Density Shape Context algorithm (LDSC). In LDSC, the point local density is firstly used for cutting down the negative impacting on extracting the feature descriptor. And the optimization of pairwise matching is firstly used in LDSC for improving the effectiveness. To demonstrate the performance of LDSC, we conduct experiments on synthetic datasets and real world datasets. The experimental results indicate that LDSC outperforms the three compared classical methods in most cases. LDSC is robust to outliers and noise
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