Intelligent photo clustering with user interaction and distance metric learning

Wang, Meng; Ji, Dinghuang; Hua, Xian-Sheng

doi:10.1016/j.patrec.2011.02.012

Cited by 12 publications

(2 citation statements)

References 26 publications

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“…Similarity measurement is a fundamental problem in machine learning and computer vision domain [6], [24], [25], [26], [27], [28], [29], [30]. In comparison with traditional fixed metrics, e.g., euclidean and Mahalanobis distance, metric learning [1], [2], [3], [4] is the task of learning a distance function over objects.…”

Section: Related Workmentioning

confidence: 99%

Online Similarity Learning for Big Data with Overfitting

Cong

Liu

Fan

et al. 2018

IEEE Trans. Big Data

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Abstract-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 Oðd 2 Þ. 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.

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Section: Related Workmentioning

confidence: 99%

Online Similarity Learning for Big Data with Overfitting

Cong

Liu

Fan

et al. 2018

IEEE Trans. Big Data

View full text Add to dashboard Cite

show abstract

“…After Cohen et al published the first paper on this subject [15], motivated by various real applications including problems related to document retrieval, web search, recommender systems, credit-risk screening and drug discovery, ranking has been investigated intensively in machine learning [1][2][3][4]13,14,17,18,22,26,27,[40][41][42][43][44]49,52,55,56]. Consequently, much progress has been made with those techniques critical to ranking and other machine learning problems as well, e.g., Distance Metric Learning [24,54,61] and Nonnegative Matrix Factorization [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%