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Semi-supervised Online Multiple Kernel Learning Algorithm for Big Data

Ning Liu, Jianhua Zhao
2016 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
Next, an unsupervised online multiple kernel learning algorithm for big data (UOMK_bd) is proposed.  ...  In UOMK_bd, the traditional kernel learning algorithm is improved to adapt to the online environment and data replacement strategy is used to modify the kernel function in unsupervised manner.  ...  Next, an unsupervised online multiple kernel learning algorithm (UOMK_bd) is proposed to adapt to the online learning environment.  ... 
doi:10.12928/telkomnika.v14i2.2751 fatcat:nwqxuspzundtxm3rofq75e7kdy

Unsupervised Kernel Learning Vector Quantization

Kuo Lung Wu
2012 Advanced Engineering Forum  
In this paper, we propose an unsupervised kernel learning vector quantization (UKLVQ) algorithm that combines the concepts of the kernel method and traditional unsupervised learning vector quantization  ...  unsupervised learning vector quantization (ULVQ) becomes a special case of UKLVQ.  ...  of an unsupervised learning algorithm.  ... 
doi:10.4028/www.scientific.net/aef.6-7.243 fatcat:g3uor2eqdve4niqb6bdabuzfxm

Unsupervised and Online Place Recognition for Mobile Robot based on Local Features Description

S. H Tang, G. Hamami, B. Karasfi, D. Nakhaeinia
2013 Journal of Automation and Control Engineering  
In this paper, a robust appearance-based unsupervised and online place recognition algorithm, which is inspired from online sequential clustering methods, is introduced.  ...  As an alternative to the offline clustering (unsupervised learning) method, and inspire from Basic sequential clustering method [20] , this research proposes a new online sequential place clustering method  ...  In this research unsupervised and online place clustering method will be introduced for localization based on place appearance.  ... 
doi:10.12720/joace.1.1.60-64 fatcat:2wsbyspddja4hhg4y7mxrniuam

Parallel Computation Performingkernel-Based Clustering Algorithm Using Particle Swarm Optimization For The Big Data Analytics

2019 International journal of recent technology and engineering  
Existing System:Current Kernel-based clustering algorithms have few restraints concerning complexity, memory, and performance.  ...  Proposed work: in our research, we have introduced a parallel computation performing Kernel-based clustering algorithm using Particle Swarm Optimization approach.  ...  [16] proposed an unsupervised nonparametric kernel learning algorithm by the rise of the problem in kernel-based algorithms.  ... 
doi:10.35940/ijrte.b1740.078219 fatcat:v6fgzsa77vfvxpenc4dj6wubwm

Online SVM learning: from classification to data description and back

D.M.J. Tax, P. Laskov
2003 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)  
An online support vector data description algorithm enables application of the online paradigm to unsupervised learning.  ...  Furthermore, online learning can be used in the large-scale classification problems to limit the memory requirements for storage of the kernel matrix.  ...  The true potential of online learning can only be realized in the context of unsupervised learning. An important and relevant unsupervised learning problem is one-class classification [11, 14] .  ... 
doi:10.1109/nnsp.2003.1318049 dblp:conf/nnsp/TaxL03 fatcat:4s2kcbyul5hyfel5wjxoli634y

Continuous Manifold Based Adaptation for Evolving Visual Domains

Judy Hoffman, Trevor Darrell, Kate Saenko
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
Adaptation can be achieved via transforms or kernels learned between such stationary source and target subspaces.  ...  Our approach can learn to distinguish categories using training data collected at some point in the past, and continue to update its model of the categories for some time into the future, without receiving  ...  We stress that traditional online learning methods are not suitable for our problem. Online learning methods for classification use sequentially arriving data, but require that data to be labeled.  ... 
doi:10.1109/cvpr.2014.116 dblp:conf/cvpr/HoffmanDS14 fatcat:iw54yhxc3vgbnd5yh2caeyzvpq

Discriminative Fast Soft Competitive Learning [chapter]

Frank-Michael Schleif
2014 Lecture Notes in Computer Science  
Here we extend fast soft competitive learning to a discriminative and vector labeled learning algorithm for proximity data.  ...  Proximity matrices like kernels or dissimilarity matrices provide nonstandard data representations common in the life science domain.  ...  The obtained fast soft competitive learning algorithm (FSCL) is an effective approach to analyze large proximity datasets for unsupervised problems.  ... 
doi:10.1007/978-3-319-11179-7_11 fatcat:ejlp7suhwfa7hkxqgikgsmwtua

Predicting Transcription Factor Binding Sites with Convolutional Kernel Networks [article]

Dexiong Chen, Laurent Jacob, Julien Mairal
2017 bioRxiv   pre-print
Here, we introduce a hybrid approach between kernel methods and convolutional neural networks for sequences, which retains the ability of neural networks to learn good representations for a learning problem  ...  The growing amount of biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy.  ...  The relaxation makes it possible to learn the kernel from data, and we provide an unsupervised and a supervised algorithm to do so -the latter being a special case of CNNs.  ... 
doi:10.1101/217257 fatcat:4nhjht2l6rebfbt73qwzmery3m

Fuzzy-Kernel Learning Vector Quantization [chapter]

Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou
2004 Lecture Notes in Computer Science  
This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ.  ...  FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures.  ...  The unsupervised learning vector quantization (LVQ) [2] can be seen as a special case of the SOM, where the neighborhood set contains only the winner node.  ... 
doi:10.1007/978-3-540-28647-9_31 fatcat:urf7cl4bpbg5phfev35h34nsie

web-rMKL: a web server for dimensionality reduction and sample clustering of multi-view data based on unsupervised multiple kernel learning

2019 Nucleic Acids Research  
The underlying machine learning method rMKL-LPP performed best for clinical enrichment in a recent benchmark of state-of-the-art multi-view clustering algorithms.  ...  We also introduce a preprocessing tool for generating kernel matrices from tables containing numerical feature values.  ...  is a representative of unsupervised multiple kernel learning (1) .  ... 
doi:10.1093/nar/gkz422 pmid:31114892 pmcid:PMC6602472 fatcat:emza2nvwtnbwza5oku4h7vw4lu

Nyström Sketches [chapter]

Daniel J. Perry, Braxton Osting, Ross T. Whitaker
2017 Lecture Notes in Computer Science  
Despite prolific success, kernel methods become difficult to use in many large scale unsupervised problems because of the evaluation and storage of the full Gram matrix.  ...  We further demonstrate how this method can be used in an online setting and explore a simple extension to make the method robust to outliers in the training data.  ...  to be used in an online setting, as detailed in Algorithm 3.  ... 
doi:10.1007/978-3-319-71249-9_26 fatcat:23us77nyhzelnarp73tuxa4mz4

Online Learning: A Comprehensive Survey [article]

Steven C.H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao
2018 arXiv   pre-print
where full feedback information is always available, (ii) online learning with limited feedback, and (iii) unsupervised online learning where there is no feedback available.  ...  Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis  ...  Unsupervised learning tasks: Online learning algorithms can be applied for unsupervised learning tasks.  ... 
arXiv:1802.02871v2 fatcat:mqorsb4gknhfhjfb4jcsvbrtwm

A Survey on SVM Classifiers for Intrusion Detection

R. RavinderReddy, B. Kavya, Y Ramadevi
2014 International Journal of Computer Applications  
exact online learning.  ...  the unsupervised learning.  ... 
doi:10.5120/17294-7779 fatcat:kyj36tilq5bntalx2adtquv3xa

Editors' Introduction [chapter]

Marcus Hutter, Rocco A. Servedio, Eiji Takimoto
2007 Lecture Notes in Computer Science  
, online learning and defensive forecasting, and kernel methods.  ...  Avrim Blum works on learning theory, online algorithms, approximation algorithms, and algorithmic game theory.  ...  Busuttil and Kalnishkan also analyze a kernel version of the algorithm and prove bounds on its square loss. Unsupervised Learning.  ... 
doi:10.1007/978-3-540-75225-7_1 fatcat:jjzpiql74jac3cvxixuyav4foy

Slow feature analysis with spiking neurons and its application to audio stimuli

Guillaume Bellec, Mathieu Galtier, Romain Brette, Pierre Yger
2016 Journal of Computational Neuroscience  
Abstract Extracting invariant features in an unsupervised manner is crucial to perform complex computation such as object recognition, analyzing music or understanding speech.  ...  We show that a single SFA neuron can learn to extract the tempo in sound recordings.  ...  Left column shows the output pattern after convergence of the batch algorithm, right shows the results obtained with the online algorithm. A SFA kernel as a second derivative.  ... 
doi:10.1007/s10827-016-0599-3 pmid:27075919 fatcat:4dm2tezinvge3g2tcj55hwupmq
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