13,719 research outputs found
BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification
Active learning algorithms automatically identify the salient
and exemplar instances from large amounts of unlabeled
data and thus reduce human annotation effort in inducing
a classification model. More recently, Batch Mode Active
Learning (BMAL) techniques have been proposed, where a
batch of data samples is selected simultaneously from an un-
labeled set. Most active learning algorithms assume a
at
label space, that is, they consider the class labels to be in-
dependent. However, in many applications, the set of class
labels are organized in a hierarchical tree structure, with
the leaf nodes as outputs and the internal nodes as clusters
of outputs at multiple levels of granularity. In this paper,
we propose a novel BMAL algorithm (BatchRank) for hi-
erarchical classification. The sample selection is posed as
an NP-hard integer quadratic programming problem and a
convex relaxation (based on linear programming) is derived,
whose solution is further improved by an iterative truncated
power method. Finally, a deterministic bound is established
on the quality of the solution. Our empirical results on sev-
eral challenging, real-world datasets from multiple domains,
corroborate the potential of the proposed framework for real-
world hierarchical classification applications
Highly Efficient Regression for Scalable Person Re-Identification
Existing person re-identification models are poor for scaling up to large
data required in real-world applications due to: (1) Complexity: They employ
complex models for optimal performance resulting in high computational cost for
training at a large scale; (2) Inadaptability: Once trained, they are
unsuitable for incremental update to incorporate any new data available. This
work proposes a truly scalable solution to re-id by addressing both problems.
Specifically, a Highly Efficient Regression (HER) model is formulated by
embedding the Fisher's criterion to a ridge regression model for very fast
re-id model learning with scalable memory/storage usage. Importantly, this new
HER model supports faster than real-time incremental model updates therefore
making real-time active learning feasible in re-id with human-in-the-loop.
Extensive experiments show that such a simple and fast model not only
outperforms notably the state-of-the-art re-id methods, but also is more
scalable to large data with additional benefits to active learning for reducing
human labelling effort in re-id deployment
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