Multimodal Sparse Classifier for Adolescent Brain Age Prediction
نویسندگان
چکیده
منابع مشابه
A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference
Despite the clear potential benefits of combining fMRI and diffusion MRI in learning the neural pathways that underlie brain functions, little methodological progress has been made in this direction. In this paper, we propose a novel multimodal integration approach based on sparse Gaussian graphical model for estimating brain connectivity. Casting functional connectivity estimation as a sparse ...
متن کاملFace Recognition in Thermal Images based on Sparse Classifier
Despite recent advances in face recognition systems, they suffer from serious problems because of the extensive types of changes in human face (changes like light, glasses, head tilt, different emotional modes). Each one of these factors can significantly reduce the face recognition accuracy. Several methods have been proposed by researchers to overcome these problems. Nonetheless, in recent ye...
متن کاملAdaptive classifier cascade for multimodal speaker identification
We present a multimodal open-set speaker identification system that integrates information coming from audio, face and lip motion modalities. For fusion of multiple modalities, we propose a new adaptive cascade rule that favors reliable modality combinations through a cascade of classifiers. The order of the classifiers in the cascade is adaptively determined based on the reliability of each mo...
متن کاملMultimodal Sparse Features for Object Detection
In this paper the sparse coding principle is employed for the representation of multimodal image data, i.e. image intensity and range. We estimate an image basis for frontal face images taken with a Time-ofFlight (TOF) camera to obtain a sparse representation of facial features, such as the nose. These features are then evaluated in an object detection scenario where we estimate the position of...
متن کاملMultimodal Sparse Coding for Event Detection
Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature learning methods such ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Journal of Biomedical and Health Informatics
سال: 2020
ISSN: 2168-2194,2168-2208
DOI: 10.1109/jbhi.2019.2925710