The microblogging services become increasingly popular for people to exchange their feelings and opinions. Extracting and analyzing the sentiments in microblogs have drawn extensive attentions from both academia researchers and commercial companies. The previous literature usually focused on classifying the microblogs into positive or negative categories. However, people’s sentiments are much m...

Multicast support in a Multipro-tocol Label Switching (MPLS) network has yet to be deened. An MPLS network consists of label switching devices such as ATM. This document discusses both dense-mode and sparse-mode IP multicast within the context of MPLS networks. Unlike unicast routing, dense-mode multicast routing trees are established in a data-driven manner and it is not possible to topologica...

Instagram has more than 400 million monthly active accounts who share more than 80 million pictures and videos daily. This large volume of user-generated content is the application’s notable strength, but also makes the problem of finding the authoritative users for a given topic challenging. Discovering topical authorities can be useful for providing relevant recommendations to the users. In a...

We consider multi-label prediction problems with large output spaces under the assumption of output sparsity – that the target (label) vectors have small support. We develop a general theory for a variant of the popular error correcting output code scheme, using ideas from compressed sensing for exploiting this sparsity. The method can be regarded as a simple reduction from multi-label regressi...

We present a supervised multi-label classification method for automatic image annotation. Our method estimates the annotation labels for a test image by accumulating similarities between the test image and labeled training images. The similarities are measured on the basis of sparse representation of the test image by the training images, which avoids similarity votes for irrelevant classes. Be...

A site-specific and quantitative approach for label-free monitoring of molecular recognition is presented. Specifically, the binding site of an artificial receptor is probed selectively by UVRR spectroscopy. The ligand binding constant can be determined by non-negative matrix factorization.

When new data streaming arrives, traditional hashing methods should retrain the functions based on all samples. That leads to high training time complexity. In contrast, online algorithm re-computes just arrival and has been widely applied in large-scale image retrieval tasks. However, differences exist numbers labels between old datasets, which causes imbalance problem while establishing their...

Many web-based application areas must infer label distributions starting from a small set of sparse, noisy labels. Examples include searching for, recommending, and advertising against image, audio, and video content. These labeling problems must handle millions of interconnected entities (users, domains, content segments) and thousands of competing labels (interests, tags, recommendations, top...

Graph based semi-supervised learning (GSSL) has intuitive representation and can be improved by exploiting the matrix calculation. However, it has to perform iterative optimization to achieve a preset objective, which usually leads to low efficiency. Another inconvenience lying in GSSL is that when new data come, the graph construction and the optimization have to be conducted all over again. W...