Infinite-Label Learning with Semantic Output Codes
arXiv:1608.06608
Abstract
We develop a new statistical machine learning paradigm, named infinite-label learning, to annotate a data point with more than one relevant labels from a candidate set, which pools both the finite labels observed at training and a potentially infinite number of previously unseen labels. The infinite-label learning fundamentally expands the scope of conventional multi-label learning, and better models the practical requirements in various real-world applications, such as image tagging, ads-query association, and article categorization. However, how can we learn a labeling function that is capable of assigning to a data point the labels omitted from the training set? To answer the question, we seek some clues from the recent work on zero-shot learning, where the key is to represent a class/label by a vector of semantic codes, as opposed to treating them as atomic labels. We validate the infinite-label learning by a PAC bound in theory and some empirical studies on both synthetic and real data.
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Multi-Label Prediction via Compressed Sensing
- A Unified Perspective on Multi-Domain and Multi-Task Learning
- An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
- Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
- Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method