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Single Cell Self-Paced Clustering with Transscriptome Sequencing Data

Peng Zhao, Zenglin Xu, Junjie Chen, Yazhou Ren, Irwin King
2022 International Journal of Molecular Sciences  
To address this issue, we introduce a single cell self-paced clustering (scSPaC) method with F-norm based nonnegative matrix factorization (NMF) for scRNA-seq data and a sparse single cell self-paced clustering  ...  (sscSPaC) method with l21-norm based nonnegative matrix factorization for scRNA-seq data.  ...  Equation ( 12 ) is a soft weighting strategy.  ... 
doi:10.3390/ijms23073900 pmid:35409258 pmcid:PMC8999118 fatcat:pavi4rfz2na2tj2wxkwzt2yahe

Unsupervised feature selection via self-paced learning and low-redundant regularization [article]

Weiyi Li, Hongmei Chen, Tianrui Li, Jihong Wan, Binbin Sang
2021 arXiv   pre-print
L_2,1/2-norm is applied to the projection matrix, which aims to retain discriminative features and further alleviate the effect of noise in the data.  ...  In this study, an unsupervised feature selection is proposed by integrating the framework of self-paced learning and subspace learning.  ...  (2019YFH0097), and Sichuan Key R&D project (2020YFG0035).  ... 
arXiv:2112.07227v1 fatcat:eqswa4uez5ghngbkonjrl3pg2q

New Approaches in Multi-View Clustering [chapter]

Fanghua Ye, Zitai Chen, Hui Qian, Rui Li, Chuan Chen, Zibin Zheng
2018 Recent Applications in Data Clustering  
learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning.  ...  These clustering methods are the most widely employed algorithms for single-view data, and lots of efforts have been devoted to extending them for multi-view clustering.  ...  The general self-paced learning model consists of a weighted loss function on all data samples and a regularizer term imposed on the weights of data samples.  ... 
doi:10.5772/intechopen.75598 fatcat:jniifuf4ync27fofz4fpbnfiia

A Survey on Multi-View Clustering [article]

Guoqing Chao, Shiliang Sun, Jinbo Bi
2018 arXiv   pre-print
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.  ...  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.  ...  ACKNOWLEDGMENT This work was supported by National Institutes of Health (NIH) grants R01DA037349 and K02DA043063, and National Science Foundation (NSF) grants DBI-1356655, CCF-1514357, and IIS-1718738.  ... 
arXiv:1712.06246v2 fatcat:w3b2hfnqyzbbbfcz6t3gl5mlny

A Simple General Approach to Balance Task Difficulty in Multi-Task Learning [article]

Sicong Liang, Yu Zhang
2020 arXiv   pre-print
Those approaches have their own limitations, for example, using manually designed rules to update task weights, non-smooth objective function, and failing to incorporate other functions than training losses  ...  There are many works to handle this situation and we classify them into five categories, including the direct sum approach, the weighted sum approach, the maximum approach, the curriculum learning approach  ...  a normalization factor to ensure m i=1 w i = m, and T is a temperature parameter to control the softness of task weights.  ... 
arXiv:2002.04792v1 fatcat:bfoe6wb24zawxlvj2bhkhd2oci

Low-Rank Tensor Thresholding Ridge Regression

Kailing Guo, Tong Zhang, Xiangmin Xu, Xiaofen Xing
2019 IEEE Access  
In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years.  ...  At the same time, we remove noise of the data in both the input space and the projection space, and obtain a robust affinity matrix for spectral clustering.  ...  LT-MSC applies matrix self representation on each view of the data and forms all the representation coefficients as a low-rank tensor.  ... 
doi:10.1109/access.2019.2944426 fatcat:56fatkxvabbzrchvsdotsjjzp4

Strongly Augmented Contrastive Clustering [article]

Xiaozhi Deng, Dong Huang, Ding-Hua Chen, Chang-Dong Wang, Jian-Huang Lai
2022 arXiv   pre-print
jointly leverages strong and weak augmentations for strengthened deep clustering.  ...  Based on the representations produced by the backbone, the weak-weak view pair and the strong-weak view pairs are simultaneously exploited for the instance-level contrastive learning (via an instance projector  ...  [24] developed an adaptive self-paced deep clustering with data augmentation (ASPC-DA) method that incorporates data augmentation and self-paced learning into deep clustering.  ... 
arXiv:2206.00380v2 fatcat:4klq6zqc2vf2fa33axier5xsu4

Theta phase coding in a network model of the entorhinal cortex layer II with entorhinal-hippocampal loop connections

Jun Igarashi, Hatsuo Hayashi, Katsumi Tateno
2006 Cognitive Neurodynamics  
NMF seeks a decomposition of a nonnegative data matrix into a product of two factor matrices (basis matrix and encoding matrix) such that all factor matrices are forced to be nonnegative.  ...  Among those, nonnegative matrix factorization (NMF) has recently drawn extensive attention, since promising results were reported in handling nonnegative data such as document, image data, spectrograms  ...  The proposed algorithms can be implemented in pixel-parallel, and they use only the neighboring pixel states, they are suitable for the VLSI implementation.  ... 
doi:10.1007/s11571-006-9003-8 pmid:19003510 pmcid:PMC2267667 fatcat:avihqfr6a5e7tm43lythi27n3a

A Survey of Label-noise Representation Learning: Past, Present and Future [article]

Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama
2021 arXiv   pre-print
Classical machine learning implicitly assumes that labels of the training data are sampled from a clean distribution, which can be too restrictive for real-world scenarios.  ...  Therefore, it is urgent to design Label-Noise Representation Learning (LNRL) methods for robustly training deep models with noisy labels. To fully understand LNRL, we conduct a survey study.  ...  First, we can combine self-training with memorization effects, which brings us self-paced Men-torNet and learning to reweight.  ... 
arXiv:2011.04406v2 fatcat:76np6wyzvvag7ehy23cwyzdozm

High-throughput Genetic Clustering of Type 2 Diabetes Loci Reveals Heterogeneous Mechanistic Pathways of Metabolic Disease [article]

Hyunkyung Kim, Kenneth E. Westerman, Kirk Smith, Joshua Chiou, Joanne B. Cole, Timothy Majarian, Marcin von Grotthuss, Josep M. Mercader, Soo Heon Kwak, Jaegil Kim, Jose C. Florez, Kyle Gaulton (+2 others)
2022 medRxiv   pre-print
Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for T2D and related traits to generate a matrix of 324 variant x 64 trait associations and applied Bayesian Non-negative  ...  Factorization (bNMF) to identify genetic components of T2D.  ...  While conventional nonnegative matrix factorization (NMF) requires the desired model order K as an input, bNMF determines an optimal K which best balances between an error measure ||X−WH|| 2 and a penalty  ... 
doi:10.1101/2022.07.11.22277436 fatcat:ipg5fgxbgvfixnt5dn3c6kcvya

2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32

2021 IEEE Transactions on Neural Networks and Learning Systems  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, TNNLS May 2021 1974-1988 Big Data Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models.  ... 
doi:10.1109/tnnls.2021.3134132 fatcat:2e7comcq2fhrziselptjubwjme

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

Maikel León, Gonzalo Nápoles, Rafael Bello, Lusine Mkrtchyan, Benoît Depaire, Koen Vanhoof
2013 International Journal of Computational Intelligence Systems  
For the creation of knowledge bases the use of Knowledge Engineering is accounted and later on the data is transferred into structures based on Fuzzy Cognitive Maps.  ...  An automatic approach to extract mental representations from individuals and convert them into computational structures is defined.  ...  of the n concepts and, a weight matrix (adjacency matrix) W which gathers the weights W ij of the interconnections among the n concepts [66] .  ... 
doi:10.1080/18756891.2013.816025 fatcat:nfedg2d425firag3ypezlhwa2u

Low-rank Regularized Multimodal Representation for Micro-video Event Detection

Jing Zhang, Yuting Wu, Jinghui Liu, Peiguang Jing, Yuting Su
2020 IEEE Access  
Although the length of micro-videos is limited to cater to the fast pace of life and are beneficial for rapid distribution, micro-videos are usually recorded in specific scenarios and tend to convey relatively  ...  between representations and their correspondences.  ...  for the i-th view, S k is the weight matrix computed by the Gaussian similarity function, and D k is the diagonal degree matrix with D k ii = j S k ij .  ... 
doi:10.1109/access.2020.2992436 fatcat:wsagxo6qirftxcetqlqshzf26m

Chemical and valence reconstruction at the surface of SmB6 revealed by means of resonant soft x-ray reflectometry

V. B. Zabolotnyy, K. Fürsich, R. J. Green, P. Lutz, K. Treiber, Chul-Hee Min, A. V. Dukhnenko, N. Y. Shitsevalova, V. B. Filipov, B. Y. Kang, B. K. Cho, R. Sutarto (+4 others)
2018 Physical review B  
Here, we use a combination of x-ray absorption and reflectometry techniques, backed up with a theoretical model for the resonant M_4,5 absorption edge of Sm and photoemission data, to establish laterally  ...  Our findings reconcile earlier depth resolved photoemission and scanning tunneling spectroscopy studies performed at different temperatures and are important for better control of polarity and, as a consequence  ...  This is further validated by taking into account that TEY data exhibited little evidence for non-linearity, and as a consequence, for the saturation and self-absorption effects that could have drawn the  ... 
doi:10.1103/physrevb.97.205416 fatcat:fmih3aa5ujdhjfugppwhlg4qti

Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data [article]

Shuo Yang
2018 arXiv   pre-print
With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine  ...  with similar genetic factors, location or socio-demographic background.  ...  For example, on WebKB data, RFGB with re-sampling achieved weighted-AUC of 0.41 compared to 0.48 for RFGB without re-sampling and 0.50 for soft RFGB; re-sampling 0.43, RFGB without re-sampling 0.45 and  ... 
arXiv:1811.00749v1 fatcat:5tbyk62ahjh3zkosrwem7picpi
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