35,245 research outputs found
Mistake-Driven Learning in Text Categorization
Learning problems in the text processing domain often map the text to a space
whose dimensions are the measured features of the text, e.g., its words. Three
characteristic properties of this domain are (a) very high dimensionality, (b)
both the learned concepts and the instances reside very sparsely in the feature
space, and (c) a high variation in the number of active features in an
instance. In this work we study three mistake-driven learning algorithms for a
typical task of this nature -- text categorization. We argue that these
algorithms -- which categorize documents by learning a linear separator in the
feature space -- have a few properties that make them ideal for this domain. We
then show that a quantum leap in performance is achieved when we further modify
the algorithms to better address some of the specific characteristics of the
domain. In particular, we demonstrate (1) how variation in document length can
be tolerated by either normalizing feature weights or by using negative
weights, (2) the positive effect of applying a threshold range in training, (3)
alternatives in considering feature frequency, and (4) the benefits of
discarding features while training. Overall, we present an algorithm, a
variation of Littlestone's Winnow, which performs significantly better than any
other algorithm tested on this task using a similar feature set.Comment: 9 pages, uses aclap.st
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often establish the shared embedding space based on linear mapping
functions which might not be sophisticated enough to reveal more complicated
inter-modal correspondences. Additionally, current studies assume that the
rankings are of equal importance, and thus all rankings are used
simultaneously, or a small number of rankings are selected randomly to train
the embedding space at each iteration. Such strategies, however, always suffer
from outliers as well as reduced generalization capability due to their lack of
insightful understanding of procedure of human cognition. In this paper, we
involve the self-paced learning theory with diversity into the cross-modal
learning to rank and learn an optimal multi-modal embedding space based on
non-linear mapping functions. This strategy enhances the model's robustness to
outliers and achieves better generalization via training the model gradually
from easy rankings by diverse queries to more complex ones. An efficient
alternative algorithm is exploited to solve the proposed challenging problem
with fast convergence in practice. Extensive experimental results on several
benchmark datasets indicate that the proposed method achieves significant
improvements over the state-of-the-arts in this literature.Comment: 14 pages; Accepted by Computer Vision and Image Understandin
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