Multi-View Multi-Label Learning With View-Label-Specific Features
نویسندگان
چکیده
منابع مشابه
Multi-view, Multi-label Learning with Deep Neural Networks
Deep learning is a popular technique in modern online and offline services. Deep neural network based learning systems have made groundbreaking progress in model size, training and inference speed, and expressive power in recent years, but to tailor the model to specific problems and exploit data and problem structures is still an ongoing research topic. We look into two types of deep ‘‘multi-’...
متن کاملLabeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning
Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Networ...
متن کاملMulti-view Weak-label Learning based on Matrix Completion∗
Weak-label learning is an important branch of multi-label learning; it deals with samples annotated with incomplete (weak) labels. Previous work on weak-label learning mainly considers data represented by a single view. An intuitive way to leverage multiple features obtained from different views is to concatenate the features into a single vector. However, this process is not only prone to over...
متن کاملMulti-Label Learning with Weak Label
Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the “full” label set associated with each training instance is given by users. In many applications, however, to get the full label set for each instance is difficult and only a “partial” set of labels is available. In such cases, the appeara...
متن کاملMulti-View Budgeted Learning under Label and Feature Constraints Using Label-Guided Graph-Based Regularization
Budgeted learning under constraints on both the amount of labeled information and the availability of features at test time pertains to a large number of real world problems. Ideas from multi-view learning, semisupervised learning, and even active learning have applicability, but a common framework whose assumptions fit these problem spaces is non-trivial to construct. We leverage ideas from th...
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 Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2930468