33,363 research outputs found
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets
Binary and nonbinary description of hypointensity for search and retrieval of brain MR images
Diagnosis accuracy in the medical field, is mainly affected by either lack of sufficient understanding of some diseases or the inter/intra-observer variability of the diagnoses. We believe that mining of large medical databases can help improve the current status of disease understanding and decision making. In a previous study based on binary description of hypointensity in the brain, it was shown that brain iron accumulation shape provides additional information to the shape-insensitive features, such as the total brain iron load, that are commonly used in clinics. This paper proposes a novel, nonbinary description of hypointensity in the brain based on principal component analysis. We compare the complementary and redundant information provided by the two descriptions using Kendall's rank correlation coefficient in order to better understand the individual descriptions of iron accumulation in the brain and obtain a more robust and accurate search and retrieval system
Stable Feature Selection for Biomarker Discovery
Feature selection techniques have been used as the workhorse in biomarker
discovery applications for a long time. Surprisingly, the stability of feature
selection with respect to sampling variations has long been under-considered.
It is only until recently that this issue has received more and more attention.
In this article, we review existing stable feature selection methods for
biomarker discovery using a generic hierarchal framework. We have two
objectives: (1) providing an overview on this new yet fast growing topic for a
convenient reference; (2) categorizing existing methods under an expandable
framework for future research and development
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
Feature selection is playing an increasingly significant role with respect to
many computer vision applications spanning from object recognition to visual
object tracking. However, most of the recent solutions in feature selection are
not robust across different and heterogeneous set of data. In this paper, we
address this issue proposing a robust probabilistic latent graph-based feature
selection algorithm that performs the ranking step while considering all the
possible subsets of features, as paths on a graph, bypassing the combinatorial
problem analytically. An appealing characteristic of the approach is that it
aims to discover an abstraction behind low-level sensory data, that is,
relevancy. Relevancy is modelled as a latent variable in a PLSA-inspired
generative process that allows the investigation of the importance of a feature
when injected into an arbitrary set of cues. The proposed method has been
tested on ten diverse benchmarks, and compared against eleven state of the art
feature selection methods. Results show that the proposed approach attains the
highest performance levels across many different scenarios and difficulties,
thereby confirming its strong robustness while setting a new state of the art
in feature selection domain.Comment: Accepted at the IEEE International Conference on Computer Vision
(ICCV), 2017, Venice. Preprint cop
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