2 research outputs found
Large Scale Local Online Similarity/Distance Learning Framework based on Passive/Aggressive
Similarity/Distance measures play a key role in many machine learning,
pattern recognition, and data mining algorithms, which leads to the emergence
of metric learning field. Many metric learning algorithms learn a global
distance function from data that satisfy the constraints of the problem.
However, in many real-world datasets that the discrimination power of features
varies in the different regions of input space, a global metric is often unable
to capture the complexity of the task. To address this challenge, local metric
learning methods are proposed that learn multiple metrics across the different
regions of input space. Some advantages of these methods are high flexibility
and the ability to learn a nonlinear mapping but typically achieves at the
expense of higher time requirement and overfitting problem. To overcome these
challenges, this research presents an online multiple metric learning
framework. Each metric in the proposed framework is composed of a global and a
local component learned simultaneously. Adding a global component to a local
metric efficiently reduce the problem of overfitting. The proposed framework is
also scalable with both sample size and the dimension of input data. To the
best of our knowledge, this is the first local online similarity/distance
learning framework based on PA (Passive/Aggressive). In addition, for
scalability with the dimension of input data, DRP (Dual Random Projection) is
extended for local online learning in the present work. It enables our methods
to be run efficiently on high-dimensional datasets, while maintains their
predictive performance. The proposed framework provides a straightforward local
extension to any global online similarity/distance learning algorithm based on
PA
Low-Rank Robust Online Distance/Similarity Learning based on the Rescaled Hinge Loss
An important challenge in metric learning is scalability to both size and
dimension of input data. Online metric learning algorithms are proposed to
address this challenge. Existing methods are commonly based on (Passive
Aggressive) PA approach. Hence, they can rapidly process large volumes of data
with an adaptive learning rate. However, these algorithms are based on the
Hinge loss and so are not robust against outliers and label noise. Also,
existing online methods usually assume training triplets or pairwise
constraints are exist in advance. However, many datasets in real-world
applications are in the form of input data and their associated labels. We
address these challenges by formulating the online Distance-Similarity learning
problem with the robust Rescaled hinge loss function. The proposed model is
rather general and can be applied to any PA-based online Distance-Similarity
algorithm. Also, we develop an efficient robust one-pass triplet construction
algorithm. Finally, to provide scalability in high dimensional DML
environments, the low-rank version of the proposed methods is presented that
not only reduces the computational cost significantly but also keeps the
predictive performance of the learned metrics. Also, it provides a
straightforward extension of our methods for deep Distance-Similarity learning.
We conduct several experiments on datasets from various applications. The
results confirm that the proposed methods significantly outperform
state-of-the-art online DML methods in the presence of label noise and outliers
by a large margin.Comment: An Online Distance-Similarity learning approach in noisy environmen