6,519 research outputs found
Classification via Incoherent Subspaces
This article presents a new classification framework that can extract
individual features per class. The scheme is based on a model of incoherent
subspaces, each one associated to one class, and a model on how the elements in
a class are represented in this subspace. After the theoretical analysis an
alternate projection algorithm to find such a collection is developed. The
classification performance and speed of the proposed method is tested on the AR
and YaleB databases and compared to that of Fisher's LDA and a recent approach
based on on minimisation. Finally connections of the presented scheme
to already existing work are discussed and possible ways of extensions are
pointed out.Comment: 22 pages, 2 figures, 4 table
Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation
This paper introduces a new Bayesian approach to the inverse problem of
passive microwave rainfall retrieval. The proposed methodology relies on a
regularization technique and makes use of two joint dictionaries of
coincidental rainfall profiles and their corresponding upwelling spectral
radiative fluxes. A sequential detection-estimation strategy is adopted, which
basically assumes that similar rainfall intensity values and their spectral
radiances live close to some sufficiently smooth manifolds with analogous local
geometry. The detection step employs a nearest neighborhood classification
rule, while the estimation scheme is equipped with a constrained shrinkage
estimator to ensure stability of retrieval and some physical consistency. The
algorithm is examined using coincidental observations of the active
precipitation radar (PR) and passive microwave imager (TMI) on board the
Tropical Rainfall Measuring Mission (TRMM) satellite. We present promising
results of instantaneous rainfall retrieval for some tropical storms and
mesoscale convective systems over ocean, land, and coastal zones. We provide
evidence that the algorithm is capable of properly capturing different storm
morphologies including high intensity rain-cells and trailing light rainfall,
especially over land and coastal areas. The algorithm is also validated at an
annual scale for calendar year 2013 versus the standard (version 7) radar
(2A25) and radiometer (2A12) rainfall products of the TRMM satellite
Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods.
Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems
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