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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Online Similarity Learning for Big Data with Overfitting
ترجمه فارسی عنوان مقاله:
یادگیری شباهت آنلاین برای داده های بزرگ با Overfitting
منبع:
IEEE - IEEE TRANSACTIONS ON BIG DATA, VOL: 4, NO: 1, JANUARY-MARCH 2018
نویسنده:
Yang Cong،Ji Liu, Baojie Fan, Peng Zeng , Haibin Yu, and Jiebo Luo,
چکیده انگلیسی:
In this paper, we propose a general model to address the overfitting problem in online similarity learning for big data, which
is generally generated by two kinds of redundancies: 1) feature redundancy, that is there exists redundant (irrelevant) features in the
training data; 2) rank redundancy, that is non-redundant (or relevant) features lie in a low rank space. To overcome these, our model is
designed to obtain a simple and robust metric matrix through detecting the redundant rows and columns in the metric matrix and
constraining the remaining matrix to a low rank space. To reduce feature redundancy, we employ the group sparsity regularization, i.e.,
the ‘2;1 norm, to encourage a sparse feature set. To address rank redundancy, we adopt the low rank regularization, the max norm,
instead of calculating the SVD as in traditional models using the nuclear norm. Therefore, our model can not only generate a low rank
metric matrix to avoid overfitting, but also achieves feature selection simultaneously. For model optimization, an online algorithm based
on the stochastic proximal method is derived to solve this problem efficiently with the complexity of Oðd2Þ. To validate the effectiveness
and efficiency of our algorithms, we apply our model to online scene categorization and synthesized data and conduct experiments on
various benchmark datasets with comparisons to several state-of-the-art methods. Our model is as efficient as the fastest online
similarity learning model OASIS, while performing generally as well as the accurate model OMLLR. Moreover, our model can exclude
irrelevant / redundant feature dimension simultaneously.
Index Terms: Online learning, similarity learning, low rank, sparse representation, feature selection, overfitting, redundancy, big data
قیمت: رایگان
توضیحات اضافی:
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