Why Size Matters: Feature Coding as Nystrom Sampling
نویسندگان
چکیده
Recently, the computer vision and machine learning community has been in favor of feature extraction pipelines that rely on a coding step followed by a linear classifier, due to their overall simplicity, well understood properties of linear classifiers, and their computational efficiency. In this paper we propose a novel view of this pipeline based on kernel methods and Nyström sampling. In particular, we focus on the coding of a data point with a local representation based on a dictionary with fewer elements than the number of data points, and view it as an approximation to the actual function that would compute pair-wise similarity to all data points (often too many to compute in practice), followed by a Nyström sampling step to select a subset of all data points.
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عنوان ژورنال:
- CoRR
دوره abs/1301.5348 شماره
صفحات -
تاریخ انتشار 2013