33,161 research outputs found
The Nature of Novelty Detection
Sentence level novelty detection aims at reducing redundant sentences from a
sentence list. In the task, sentences appearing later in the list with no new
meanings are eliminated. Aiming at a better accuracy for detecting redundancy,
this paper reveals the nature of the novelty detection task currently
overlooked by the Novelty community Novelty as a combination of the partial
overlap (PO, two sentences sharing common facts) and complete overlap (CO, the
first sentence covers all the facts of the second sentence) relations. By
formalizing novelty detection as a combination of the two relations between
sentences, new viewpoints toward techniques dealing with Novelty are proposed.
Among the methods discussed, the similarity, overlap, pool and language
modeling approaches are commonly used. Furthermore, a novel approach, selected
pool method is provided, which is immediate following the nature of the task.
Experimental results obtained on all the three currently available novelty
datasets showed that selected pool is significantly better or no worse than the
current methods. Knowledge about the nature of the task also affects the
evaluation methodologies. We propose new evaluation measures for Novelty
according to the nature of the task, as well as possible directions for future
study.Comment: This paper pointed out the future direction for novelty detection
research. 37 pages, double spaced versio
Cost-saving or Cost-enhancing Mergers: the Impact of the Distribution of Roles in Oligopoly
Horizontal Merger, Efficiency gains, Efficiency losses, Stackelberg oligopoly, Market power
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Hyperspectral images (HSIs) are often corrupted by a mixture of several types
of noise during the acquisition process, e.g., Gaussian noise, impulse noise,
dead lines, stripes, and many others. Such complex noise could degrade the
quality of the acquired HSIs, limiting the precision of the subsequent
processing. In this paper, we present a novel tensor-based HSI restoration
approach by fully identifying the intrinsic structures of the clean HSI part
and the mixed noise part respectively. Specifically, for the clean HSI part, we
use tensor Tucker decomposition to describe the global correlation among all
bands, and an anisotropic spatial-spectral total variation (SSTV)
regularization to characterize the piecewise smooth structure in both spatial
and spectral domains. For the mixed noise part, we adopt the norm
regularization to detect the sparse noise, including stripes, impulse noise,
and dead pixels. Despite that TV regulariztion has the ability of removing
Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian
noise for some real-world scenarios. Then, we develop an efficient algorithm
for solving the resulting optimization problem by using the augmented Lagrange
multiplier (ALM) method. Finally, extensive experiments on simulated and
real-world noise HSIs are carried out to demonstrate the superiority of the
proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure
An Inexact Primal-Dual Smoothing Framework for Large-Scale Non-Bilinear Saddle Point Problems
We develop an inexact primal-dual first-order smoothing framework to solve a
class of non-bilinear saddle point problems with primal strong convexity.
Compared with existing methods, our framework yields a significant improvement
over the primal oracle complexity, while it has competitive dual oracle
complexity. In addition, we consider the situation where the primal-dual
coupling term has a large number of component functions. To efficiently handle
this situation, we develop a randomized version of our smoothing framework,
which allows the primal and dual sub-problems in each iteration to be solved by
randomized algorithms inexactly in expectation. The convergence of this
framework is analyzed both in expectation and with high probability. In terms
of the primal and dual oracle complexities, this framework significantly
improves over its deterministic counterpart. As an important application, we
adapt both frameworks for solving convex optimization problems with many
functional constraints. To obtain an -optimal and
-feasible solution, both frameworks achieve the best-known oracle
complexities (in terms of their dependence on )
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