The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (ent...

A Network Service Provider (NSP) operating a label-switched networks such as ATM or Multi-Protocol Label Switching (MPLS) networks, sets up end-to-end bandwidth-guaranteed Label-Switched Paths (LSPs) to satisfy the connectivity requirements of its client networks. To make such a service highly available, the NSP may set up one or more backup LSPs for every active LSP. The backup LSPs are activa...

A network service provider (NSP) operating a label-switched networks such as ATM or multi-protocol label switching (MPLS) networks, sets up end-to-end bandwidth-guaranteed label-switched paths (LSPs) to satisfy the connectivity requirements of its client networks. To make such a service highly available the NSP may setup one or more backup LSPs for every active LSP. The backup LSPs are activate...

A challenging problem of multi-label learning is that both the label space and the model complexity will grow rapidly with the increase in the number of labels, and thus makes the available training samples insufficient for training a proper model. In this paper, we eliminate this problem by learning a mapping of each label in the feature space as a robust subspace, and formulating the predicti...

Label propagation is a popular graph-based semisupervised learning framework. So as to obtain the optimal labeling scores, the label propagation algorithm requires an inverse matrix which incurs the high computational cost ofO(n+cn), where n and c are the numbers of data points and labels, respectively. This paper proposes an efficient label propagation algorithm that guarantees exactly the sam...

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...

Clustering of class labels can be generated automatically, which is much lower quality than labels specified by human. In this paper, we propose a new enhancing document clustering method using terms of class label and term weights. The terms of class label can well represent the inherent structure of document clusters by non-negative matrix factorization (NMF). It can also improve the quality ...

A n-dimensional classification problem may be visualized in (n+1) dimensions using the class label as the (n + 1)th dimension. In such visualization, the class label provides a surface which is smooth in regions where classes are non-interlaced and rough in regions where classes are interlaced. The texture of the “class label surface” thus provides an intuitive measure of pattern classifiabilit...

Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...

Multi-label classification is a supervised learning problem that predicts multiple labels simultaneously. One of the key challenges in such tasks is modelling the correlations between multiple labels. LaCova is a decision tree multi-label classifier, that interpolates between two baseline methods: Binary Relevance (BR), which assumes all labels independent; and Label Powerset (LP), which learns...