Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Hierarchical Feature Space Using CLAss-specific Subspace
  Multiple Kernel -- Metric Learning for Classification   Metric learning for classification has been intensively studied over the last
decade. The idea is to learn a metric space induced from a normed vector space
on which data from different classes are well separated. Different measures of
the separation thus lead to various designs of the objective function in the
metric learning model. One classical metric is the Mahalanobis distance, where
a linear transformation matrix is designed and applied on the original dataset
to obtain a new subspace equipped with the Euclidean norm. The kernelized
version has also been developed, followed by Multiple-Kernel learning models.
In this paper, we consider metric learning to be the identification of the best
kernel function with respect to a high class separability in the corresponding
metric space. The contribution is twofold: 1) No pairwise computations are
required as in most metric learning techniques; 2) Better flexibility and lower
computational complexity is achieved using the CLAss-Specific (Multiple) Kernel
- Metric Learning (CLAS(M)K-ML). The proposed techniques can be considered as a
preprocessing step to any kernel method or kernel approximation technique. An
extension to a hierarchical learning structure is also proposed to further
improve the classification performance, where on each layer, the CLASMK is
computed based on a selected "marginal" subset and feature vectors are
constructed by concatenating the features from all previous layers.

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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph