NeNMF: An optimal gradient method for nonnegative matrix factorization
Nonnegative matrix factorization (NMF) is a powerful matrix decomposition technique that
approximates a nonnegative matrix by the product of two low-rank nonnegative matrix …
approximates a nonnegative matrix by the product of two low-rank nonnegative matrix …
Adaptive hypergraph learning and its application in image classification
Recent years have witnessed a surge of interest in graph-based transductive image
classification. Existing simple graph-based transductive learning methods only model the …
classification. Existing simple graph-based transductive learning methods only model the …
High-order distance-based multiview stochastic learning in image classification
How do we find all images in a larger set of images which have a specific content? Or
estimate the position of a specific object relative to the camera? Image classification …
estimate the position of a specific object relative to the camera? Image classification …
Fuzzy deep belief networks for semi-supervised sentiment classification
By embedding prior knowledge into the learning structure, this paper presents a two-step
semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment …
semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment …
Semisupervised multiview distance metric learning for cartoon synthesis
In image processing, cartoon character classification, retrieval, and synthesis are critical, so
that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon …
that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon …
Exploiting click constraints and multi-view features for image re-ranking
Image re-ranking is effective in improving performance of text-based image searches.
However, improvements from existing re-ranking algorithms are limited by two factors: one is …
However, improvements from existing re-ranking algorithms are limited by two factors: one is …
Traffic sign segmentation and classification using statistical learning methods
JM Lillo-Castellano, I Mora-Jiménez… - Neurocomputing, 2015 - Elsevier
Traffic signs are an essential part of any circulation system, and failure detection by the
driver may significantly increase the accident risk. Currently, automatic traffic sign detection …
driver may significantly increase the accident risk. Currently, automatic traffic sign detection …
Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning
Social media is emerging as a new mainstream means of interacting around online media.
Social influence mining in social networks is therefore of critical importance in real-world …
Social influence mining in social networks is therefore of critical importance in real-world …
Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts
In this paper, we present a novel approach for video-anomaly detection in crowded and
complicated scenes. The proposed approach detects anomalies based on a hierarchical …
complicated scenes. The proposed approach detects anomalies based on a hierarchical …
Pairwise constraints based multiview features fusion for scene classification
Recently, we have witnessed a surge of interests of learning a low-dimensional subspace
for scene classification. The existing methods do not perform well since they do not consider …
for scene classification. The existing methods do not perform well since they do not consider …