Fast generalization rates for distance metric learning
نویسندگان
چکیده
منابع مشابه
Robustness and generalization for metric learning
Metric learning has attracted a lot of interest over the last decade, but little work has been done about the generalization ability of such methods. In this paper, we address this issue by proposing an adaptation of the notion of algorithmic robustness, previously introduced by Xu and Mannor in classic supervised learning, to derive generalization bounds for metric learning. We also show that ...
متن کاملBayesian Distance Metric Learning
This thesis explores the use of Bayesian distance metric learning (Bayes-dml) for the task of speaker verification using the i-vector feature representation. We propose a framework that explores the distance constraints between i-vector pairs from the same speaker and different speakers. With an approximation of the distance metric as a weighted covariance matrix of the top eigenvectors from th...
متن کاملDistance Metric Learning Revisited
The success of many machine learning algorithms (e.g. the nearest neighborhood classification and k-means clustering) depends on the representation of the data as elements in a metric space. Learning an appropriate distance metric from data is usually superior to the default Euclidean distance. In this paper, we revisit the original model proposed by Xing et al. [24] and propose a general formu...
متن کاملHamming Distance Metric Learning
Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity. Binary codes are well suited to large-scale applications as they are storage efficient and permit exact sub-linear kNN search. The framework is applicable to broad families of mappings, and uses a flexible form of triplet ranking loss. We ov...
متن کاملDistance metric learning by minimal distance maximization
Classic linear dimensionality reduction (LDR) methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are known not to be robust against outliers. Following a systematic analysis of the multi-class LDR problem in a unified framework, we propose a new algorithm, called minimal distance maximization (MDM), to address the non-robustness issue. The principle behi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 2018
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-018-5734-0