A Fast Incremental Gaussian Mixture Model

Pinto, Rafael C.; Engel, Paulo Martins

doi:10.1371/journal.pone.0139931

Cited by 56 publications

(38 citation statements)

References 21 publications

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“…In this paper, the RBF basis centres and widths are fixed throughout learning (see below). More generally, however, various strategies exist to construct and modify them during learning [18]. Overall, the output mapping (from x t to π t ) is equivalent to a Radial Basis Function (RBF) classifier.…”

Section: Value Networkmentioning

confidence: 99%

Multitask Learning over Shared Subspaces

Menghi

Kacar

Penny

2020

Preprint

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This paper uses constructs from the field of multitask machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach. We found, as hypothesised, that subject performance was significantly higher on the second task if it shared the same subspace as the first, an advantage that played out most strongly at the beginning of the second task. Additionally, accuracy was positively correlated over subjects learning same-subspace tasks but was not correlated for those learning different-subspace tasks. These results, and other aspects of learning dynamics, were compared to the behaviour of a Neural Network model trained using sequential Bayesian inference. Human performance was found to be consistent with a Soft Parameter Sharing variant of this model that constrained representations to be similar among tasks but only when this aided learning. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning.

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Section: Value Networkmentioning

confidence: 99%

Multitask Learning over Shared Subspaces

Menghi

Kacar

Penny

2020

Preprint

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“…The experimental results showed that ILVQ was superior to other incremental algorithms in both of accuracy and compression ratio. Some incremental learning algorithms based on Gaussian mixture network were also proposed to handle streaming data classification with faster and scalable algorithms [14, 15]. In their works, the performance was evaluated in terms of classification accuracy, number of used components, and learning time but the performance along the course test was not evaluated as time goes by.…”

Section: Related Workmentioning

confidence: 99%

“…Traditional classification techniques are usually created under complete static data given. Many learning algorithms were proposed to solve data stream classification problem, directly, such as [7–15, 17, 18]. Some stream pattern classification techniques were applied to tackle problems in real world situation.…”

Section: Introductionmentioning

confidence: 99%

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

2019

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Due to the fast speed of data generation and collection from advanced equipment, the amount of data obviously overflows the limit of available memory space and causes difficulties achieving high learning accuracy. Several methods based on discard-after-learn concept have been proposed. Some methods were designed to cope with a single incoming datum but some were designed for a chunk of incoming data. Although the results of these approaches are rather impressive, most of them are based on temporally adding more neurons to learn new incoming data without any neuron merging process which can obviously increase the computational time and space complexities. Only online versatile elliptic basis function (VEBF) introduced neuron merging to reduce the space-time complexity of learning only a single incoming datum. This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities. A set of recursive functions for computing the relevant parameters of a new neuron, based on statistical confidence interval, was introduced. The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their classes. When being compared to the others in incremental-like manner, based on 11 benchmarked data sets of 150 to 581,012 samples with attributes ranging from 4 to 1,558 formed as streaming data, the proposed SCIL gave better accuracy and time in most data sets.

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“…Hence, the proposed FlexClustS and FlexClustB clustering algorithms are scalable to very large image data sets. In the reconstruction of image using the GMM compact representation, the mixture component without any assignment will be considered as spurious component and therefore be removed as it has almost no negative impact on the model quality [ 39 ].…”

Section: The Flexclust Algorithmsmentioning

confidence: 99%

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing

Leong

Ong

2017

PLoS ONE

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This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.

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A Fast Incremental Gaussian Mixture Model

Cited by 56 publications

References 21 publications

Multitask Learning over Shared Subspaces

Multitask Learning over Shared Subspaces

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing

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