NIST logo

Publication Citation: Dictionary Learning from Ambiguously Labeled Data

NIST Authors in Bold

Author(s): P J. Phillips; Yi-Chen Chen; Vishal M. Patel; Jaishanker K. Pillai; Rama Chellappa;
Title: Dictionary Learning from Ambiguously Labeled Data
Published: April 09, 2013
Abstract: We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label. The dictionary learning problem is solved using an iterative alternating algorithm. At each iteration of the algorithm, two alternating steps are performed: a confidence update and a dictionary update. The confidence of each sample is defined as the probability distribution on its ambiguous labels. The dictionaries are updated using either soft (EM-based) or hard decision rules. Extensive evaluations on existing datasets demonstrate that the proposed method performs significantly better than state-of-the-art ambiguously labeled learning approaches.
Proceedings: IEEE Conference on Computer Vision and Pattern Recognition
Location: Portland, OR
Dates: June 25-27, 2013
Research Areas: Imaging
PDF version: PDF Document Click here to retrieve PDF version of paper (1MB)