مجال التميز | تميز دراسي وبحثي |
البحوث المنشورة |
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البحث (1): | |
عنوان البحث: |
Clustering and Classification for Time Series Data in Visual Analytics: A Survey |
رابط إلى البحث: | |
تاريخ النشر: |
10/12/2019 |
موجز عن البحث: |
Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics. |
المؤتمرات العلمية |
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المؤتمر (1): | |
عنوان المؤتمر: |
25th IEEE international conference on image processing (ICIP) |
تاريخ الانعقاد: |
07/10/2018 |
مكان الانعقاد: |
Athens, Greece |
طبيعة المشاركة: |
Paper presentation |
عنوان المشاركة: |
A deep convolutional auto-encoder with embedded clustering |
ملخص المشاركة: |
In this paper, we propose a clustering approach embedded in a deep convolutional auto-encoder (DCAE). In contrast to conventional clustering approaches, our method simultaneously learns feature representations and cluster assignments through DCAEs. DCAEs have been effective in image processing as it fully utilizes the properties of convolutional neural networks. Our method consists of clustering and reconstruction objective functions. All data points are assigned to their new corresponding cluster centers during the optimization, after that, clustering centers are iteratively updated to obtain a stable performance of clustering. The experimental results on the MNIST dataset show that the proposed method substantially outperforms deep clustering models in term of clustering quality. |
الرابط: | |
المؤتمر (2): | |
عنوان المؤتمر: |
International Conference on Computer Analysis of Images and Patterns |
تاريخ الانعقاد: |
22/08/2019 |
مكان الانعقاد: |
Salerno, Italy |
طبيعة المشاركة: |
Paper presentation |
عنوان المشاركة: |
Learning discriminatory deep clustering models |
ملخص المشاركة: |
Deep convolutional auto-encoder (DCAE) allows to obtain useful features via its internal layer and provide an abstracted latent representation, which has been exploited for clustering analysis. DCAE allows a deep clustering method to extract similar patterns in lower-dimensional representation and find idealistic representative centers for distributed data. In this paper, we present a deep clustering model carried out in presence of varying degrees of supervision. We propose a new version of DCAE to include a supervision component. It introduces a mechanism to inject various levels of supervision into the learning process. This mechanism helps to effectively reconcile extracted latent representations and provided supervising knowledge in order to produce the best discriminative attributes. The key idea of our approach is distinguishing the discriminatory power of numerous structures, through varying degrees of supervision, when searching for a compact structure to form robust clusters. We evaluate our model on MNIST, USPS, MNIST fashion, SVHN datasets and show clustering accuracy on different supervisory levels. |
الرابط: | |
المؤتمر (3): | |
عنوان المؤتمر: |
25th International conference on Pattern Recognition (ICPR) |
تاريخ الانعقاد: |
10/01/2021 |
مكان الانعقاد: |
virtual |
طبيعة المشاركة: |
Paper presentation |
عنوان المشاركة: |
Neuron-based Network Pruning Based on Majority Voting |
ملخص المشاركة: |
The achievement of neural networks in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we propose an efficient method to simultaneously identify the critical neurons and prune the model during training without involving any pre-training or fine-tuning procedures. Unlike existing methods, which accomplish this task in a greedy fashion, we propose a majority voting technique to compare the activation values among neurons and assign a voting score to quantitatively evaluate their importance. This mechanism helps to effectively reduce model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Experimental results show that majority voting efficiently compresses the network with no drop in model accuracy, pruning more than 79% of the original model parameters on CIFAR10 and more than 91% of the original parameters on MNIST. Moreover, we show that with our proposed method, sparse models can be further pruned into even smaller models by removing more than 60% of the parameters, whilst preserving the reference model accuracy. |
الرابط: |
المرفقات
- https://uksacb.org/wp-content/uploads/ResearchPaper_1_DeepConvolutionalAuto-encoderWithEmbeddedClustering.pdf
- https://uksacb.org/wp-content/uploads/ResearchPaper_2_LearningDiscriminatoryDeepClustering.pdf
- https://uksacb.org/wp-content/uploads/ResearchPaper_3_ClusteringandClassificationforTimeSeries.pdf
- https://uksacb.org/wp-content/uploads/ResearchPaper_4_Neuron-basedNetworkPruningBasedOnMajority.pdf