67,990 research outputs found
On Horizontal and Vertical Separation in Hierarchical Text Classification
Hierarchy is a common and effective way of organizing data and representing
their relationships at different levels of abstraction. However, hierarchical
data dependencies cause difficulties in the estimation of "separable" models
that can distinguish between the entities in the hierarchy. Extracting
separable models of hierarchical entities requires us to take their relative
position into account and to consider the different types of dependencies in
the hierarchy. In this paper, we present an investigation of the effect of
separability in text-based entity classification and argue that in hierarchical
classification, a separation property should be established between entities
not only in the same layer, but also in different layers. Our main findings are
the followings. First, we analyse the importance of separability on the data
representation in the task of classification and based on that, we introduce a
"Strong Separation Principle" for optimizing expected effectiveness of
classifiers decision based on separation property. Second, we present
Hierarchical Significant Words Language Models (HSWLM) which capture all, and
only, the essential features of hierarchical entities according to their
relative position in the hierarchy resulting in horizontally and vertically
separable models. Third, we validate our claims on real-world data and
demonstrate that how HSWLM improves the accuracy of classification and how it
provides transferable models over time. Although discussions in this paper
focus on the classification problem, the models are applicable to any
information access tasks on data that has, or can be mapped to, a hierarchical
structure.Comment: Full paper (10 pages) accepted for publication in proceedings of ACM
SIGIR International Conference on the Theory of Information Retrieval
(ICTIR'16
A Review of Codebook Models in Patch-Based Visual Object Recognition
The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
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