Sparse Bayesian similarity learning based on posterior distribution of data

Author:
, , , , ,
Year
: 2017
Abstract: A major challenge in similarity/distance learning is attaining a strong measure which is close to human notions

of similarity. This paper shows why the consideration of data distribution can yield a more effective similarity

measure. In addition, the current work both introduces a new scalable similarity measure based on the posterior

distribution of data and develops a practical algorithm that learns the proposed measure from the data. To

address scalability in this algorithm, the observed data are assumed to have originated from low dimensional

latent variables that are close to several subspaces. Other advantages of the currently proposed method include:

(1) Providing a principled way to combine metrics in computing the similarity between new instances, unlike

local metric learning methods. (2) Automatically identifying the real dimension of latent subspaces, by defining

appropriate priors over the parameters of the system via a Bayesian framework. (3) Finding a better projection to

low dimensional subspaces, by learning the noise of the latent variables on these subspaces. The present method

is evaluated on various real datasets obtained from applications, such as face verification, handwritten digit

and spoken letter recognition, network intrusion detection, and image classification. The experimental results

confirm that the proposed method significantly outperforms other state-of-the-art metric learning methods on

both small and large-scale datasets.
Keyword(s): Similarity learning

Metric learning

Latent space

Posterior distribution

Bayesian inference
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