A survey on object detection in optical remote sensing images
Object detection in optical remote sensing images, being a fundamental but challenging
problem in the field of aerial and satellite image analysis, plays an important role for a wide …
problem in the field of aerial and satellite image analysis, plays an important role for a wide …
Feature selection based on structured sparsity: A comprehensive study
Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …
Deep multimodal feature analysis for action recognition in rgb+ d videos
Single modality action recognition on RGB or depth sequences has been extensively
explored recently. It is generally accepted that each of these two modalities has different …
explored recently. It is generally accepted that each of these two modalities has different …
Robust subspace clustering for multi-view data by exploiting correlation consensus
More often than not, a multimedia data described by multiple features, such as color and
shape features, can be naturally decomposed of multi-views. Since multi-views provide …
shape features, can be naturally decomposed of multi-views. Since multi-views provide …
Brain imaging genomics: integrated analysis and machine learning
L Shen, PM Thompson - Proceedings of the IEEE, 2019 - ieeexplore.ieee.org
Brain imaging genomics is an emerging data science field, where integrated analysis of
brain imaging and genomics data, often combined with other biomarker, clinical, and …
brain imaging and genomics data, often combined with other biomarker, clinical, and …
Multi-view clustering and feature learning via structured sparsity
Combining information from various data sources has become an important research topic
in machine learning with many scientific applications. Most previous studies employ kernels …
in machine learning with many scientific applications. Most previous studies employ kernels …
A review of fusion methods for omics and imaging data
The development of omics data and biomedical images has greatly advanced the progress
of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and …
of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and …
Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis
Multimodal data fusion has shown great advantages in uncovering information that could be
overlooked by using single modality. In this paper, we consider the integration of high …
overlooked by using single modality. In this paper, we consider the integration of high …
Heterogeneous feature selection with multi-modal deep neural networks and sparse group lasso
L Zhao, Q Hu, W Wang - IEEE Transactions on Multimedia, 2015 - ieeexplore.ieee.org
Heterogeneous feature representations are widely used in machine learning and pattern
recognition, especially for multimedia analysis. The multi-modal, often also high …
recognition, especially for multimedia analysis. The multi-modal, often also high …
Multimodal multipart learning for action recognition in depth videos
The articulated and complex nature of human actions makes the task of action recognition
difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and …
difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and …