Papers Multi-Label Learning

Logistic Boosting Regression for Label Distribution Learning

Label Distribution Learning (LDL) is a general learning framework which includes both single label and multi-label learning as its special cases. One of the main assumptions made in traditional LDL algorithms is the deri…

Streaming Label Learning for Modeling Labels on the Fly

It is challenging to handle a large volume of labels in multi-label learning. However, existing approaches explicitly or implicitly assume that all the labels in the learning process are given, which could be easily viol…

Towards Label Imbalance in Multi-label Classification with Many Labels

In multi-label classification, an instance may be associated with a set of labels simultaneously. Recently, the research on multi-label classification has largely shifted its focus to the other end of the spectrum where …

A Self-Paced Regularization Framework for Multi-Label Learning

In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into multi-label learning regime. In light o…

Asymptotic consistency and order specification for logistic classifier chains in multi-label learning

Classifier chains are popular and effective method to tackle a multi-label classification problem. The aim of this paper is to study the asymptotic properties of the chain model in which the conditional probabilities are…

LLSF - Learning Label Specific Features for Multi-Label Classifcation

Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework, …

Sparse Local Embeddings for Extreme Multi-label Classification

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches m…

Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings

We present a scalable Bayesian multi-label learning model based on learning low-dimensional label embeddings. Our model assumes that each label vector is generated as a weighted combination of a set of topics (each topic…

ML-MG: Multi-Label Learning With Missing Labels Using a Mixed Graph

This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these…

Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis

Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. …

Multi-label Text Categorization with Joint Learning Predictions-as-Features Method

Locally Non-linear Embeddings for Extreme Multi-label Learning

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches m…

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orien…

Learning with a Wasserstein Loss

Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we develop a loss function for multi-label lear…

Joint Patch and Multi-Label Learning for Facial Action Unit Detection

The face is one of the most powerful channel of non-verbal communication. The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS). FACS segments the visible effects of faci…

Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients

Automated learning of patients demographics can be seen as multi-label problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Dat…

Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning

Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then u…

Local Rademacher Complexity for Multi-label Learning

We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to…

Muli-label Text Categorization with Hidden Components

Noisy Or-based model for Relation Extraction using Distant Supervision