6,812 research outputs found
Self-Paced Learning: an Implicit Regularization Perspective
Self-paced learning (SPL) mimics the cognitive mechanism of humans and
animals that gradually learns from easy to hard samples. One key issue in SPL
is to obtain better weighting strategy that is determined by minimizer
function. Existing methods usually pursue this by artificially designing the
explicit form of SPL regularizer. In this paper, we focus on the minimizer
function, and study a group of new regularizer, named self-paced implicit
regularizer that is deduced from robust loss function. Based on the convex
conjugacy theory, the minimizer function for self-paced implicit regularizer
can be directly learned from the latent loss function, while the analytic form
of the regularizer can be even known. A general framework (named SPL-IR) for
SPL is developed accordingly. We demonstrate that the learning procedure of
SPL-IR is associated with latent robust loss functions, thus can provide some
theoretical inspirations for its working mechanism. We further analyze the
relation between SPL-IR and half-quadratic optimization. Finally, we implement
SPL-IR to both supervised and unsupervised tasks, and experimental results
corroborate our ideas and demonstrate the correctness and effectiveness of
implicit regularizers.Comment: 12 pages, 3 figure
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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