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Adaptive semi-supervised spectral clustering based on Nyström method
2010 Edition, Volume 2, October 1, 2010 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

In this paper, we proposed AS3C-N algorithm, a method of adaptive semi-supervised spectral clustering based on Nyström approximation, and apply it to color image classification. Firstly, Introduction and analysis of spectral...

A Recursive Constrained Framework for Unsupervised Video Action Clustering
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Video action understanding is an active field of intelligent video analytics, and contextual information in the videos has been gained lots of attention for better action understanding. However, most existing works focus on using contextual information for supervised or...

MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing. Optical data and radar data, two important yet intrinsically different data sources, are attracting more and more attention for potential data fusion. It is already widely known that a machine...

Hybrid Unmixing Based on Adaptive Region Segmentation for Hyperspectral Imagery
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Unmixing is an important issue of hyperspectral images. Most unmixing methods adopt linear mixing models for simplicity. However, multiple scattering usually occurs between vegetation and soil in a bilinear scene. Thus, nonlinear mixing problems which are difficult to be solved should be...

Improving the Efficiency of Land Cover Classification by Combining Segmentation, Hierarchical Clustering, and Active Learning
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Acquiring training samples for supervised machine learning methods to automate land cover classification is very labor intensive. The ever-increasing amount of available remote sensing data makes it necessary to reduce these costs. By adding a segmentation approach to hierarchical...

Graph Learning via Edge Constrained Sparse Representation for Image Analysis
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

The construction of graph essentially determines the performance of graph-based image analysis methods. Particularly, sparse graph is vital in image analysis because of its sparse and adaptive properties for enormous scale of data. However, most existing graph-based...

Restricted Boltzmann Machines With Gaussian Visible Units Guided by Pairwise Constraints
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach, without any guidances of the background knowledge. To enhance the expression ability of traditional RBMs, in this...

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds
2019 Edition, July 1, 2019 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to...

MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data
2019 Edition, Volume 57, November 1, 2019 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing. Optical data and radar data, two important yet intrinsically different data sources, are attracting more and more attention for potential data fusion. It is already widely known that a machine...

Clustering-Driven Deep Embedding With Pairwise Constraints
2019 Edition, Volume 39, July 1, 2019 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have been presented. In this paper, we depart from centroid-based models and suggest a new framework,...

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