Search Results - "Dornaika, F."

Flexible data representation with feature convolution for semi-supervised learning by Dornaika, F.

“…Data representation plays a crucial role in semi-supervised learning. This paper proposes a framework for semi-supervised data representation. It introduces a…”
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Multi-layer linear embedding with feature subset selection by Dornaika, F.

“…Many fundamental problems in machine learning require some form of dimensionality reduction. To this end, two different strategies were used: manifold learning…”
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Multi-layer manifold learning with feature selection by Dornaika, F.

“…Many fundamental problems in machine learning require some form of dimensionality reduction. To this end, two different strategies were used: Manifold Learning…”
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Towards a unified framework for graph-based multi-view clustering by Dornaika, F., El Hajjar, S.

“…Recently, clustering data collected from various sources has become a hot topic in real-world applications. The most common methods for multi-view clustering…”
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Deep learning based face beauty prediction via dynamic robust losses and ensemble regression by Bougourzi, F., Dornaika, F., Taleb-Ahmed, A.

“…In the last decade, several studies have shown that facial attractiveness can be learned by machines. In this paper, we address Facial Beauty Prediction from…”
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Linear embedding by joint Robust Discriminant Analysis and Inter-class Sparsity by Dornaika, F., Khoder, A.

“…Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. They have been used for different classification tasks…”
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Joint sparse graph and flexible embedding for graph-based semi-supervised learning by Dornaika, F., El Traboulsi, Y.

“…This letter introduces a framework for graph-based semi-supervised learning by estimating a flexible non-linear projection and its linear regression model…”
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A novel graph-based multi-view spectral clustering: application to X-ray image analysis for COVID-19 recognition by Dornaika, F., Hoang, V. Truong

“…Nowadays, machine learning tools and, in particular, classification methods are often used to diagnose COVID-19 cases. However, these methods use a single view…”
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A supervised non-linear dimensionality reduction approach for manifold learning by Raducanu, B., Dornaika, F.

“…In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label…”
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Graph Convolution Networks with manifold regularization for semi-supervised learning by Kejani, M. Tavassoli, Dornaika, F., Talebi, H.

“…In recent times, Graph Convolution Networks (GCN) have been proposed as a powerful tool for graph-based semi-supervised learning. In this paper, we introduce a…”
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Deep data representation with feature propagation for semi-supervised learning by Dornaika, F., Hoang, V. Truong

“…Graph-based embedding has attracted much attention in the fields of machine learning and pattern recognition. It is becoming an indispensable tool for data…”
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An enhanced approach to the robust discriminant analysis and class sparsity based embedding by Khoder, A., Dornaika, F.

“…In recent times, feature extraction attracted much attention in machine learning and pattern recognition fields. This paper extends and improves a scheme for…”
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Direct multi-view spectral clustering with consistent kernelized graph and convolved nonnegative representation by Dornaika, F., El Hajjar, S.

“…Multi-view clustering attempts to partition unlabeled objects into clusters by making full use of complementary and consistent information in the features of…”
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Consensus graph and spectral representation for one-step multi-view kernel based clustering by El Hajjar, S., Dornaika, F., Abdallah, F., Barrena, N.

“…Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral clustering for single and…”
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Adaptive graph construction using data self-representativeness for pattern classification by Dornaika, F., Bosaghzadeh, A.

“…Graph construction from data constitutes a pre-stage in many machine learning and computer vision tasks, like semi-supervised learning, manifold learning, and…”
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A pyramid multi-level face descriptor: application to kinship verification by Moujahid, A., Dornaika, F.

“…Texture descriptors such as Local Binary Pattern (LBP), Local Phase Quantization (LPQ), and Histogram of Oriented Gradients (HOG) have been widely used for…”
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Robust regression with deep CNNs for facial age estimation: An empirical study by Dornaika, F., Bekhouche, SE, Arganda-Carreras, I.

“…•The letter proposes deep CNN models for facial age estimation.•It opens the door for the use of robust loss functions in Deep CNNs.•Empirical evaluations are…”
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Fusing Transformed Deep and Shallow features (FTDS) for image-based facial expression recognition by Bougourzi, F., Dornaika, F., Mokrani, K., Taleb-Ahmed, A., Ruichek, Y.

“…•An improved face cropping scheme is proposed.•Deep and Shallow features are fused for facial expression recognition.•Transformed of Pyramid Multi-Level…”
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Mises-Fisher similarity-based boosted additive angular margin loss for breast cancer classification by Alirezazadeh, P., Dornaika, F., Charafeddine, J.

“…To enhance the accuracy of breast cancer diagnosis, current practices rely on biopsies and microscopic examinations. However, this approach is known for being…”
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Spatiotemporal CNN with Pyramid Bottleneck Blocks: Application to eye blinking detection by Bekhouche, S.E., Kajo, I., Ruichek, Y., Dornaika, F.

“…Eye blink detection is a challenging problem that many researchers are working on because it has the potential to solve many facial analysis tasks, such as…”
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