Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality

In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of cues. Mapping the problem on an affinity graph-where features are the nodes-the solution is given by assessing the importance of nodes through some indicators of centrality, in particular, the Eigen-vector Centrality (EC). The gist of EC is to estimate the importance of a feature as a function of the importance of its neighbors. Ranking central nodes individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. Our approach has been tested on 7 diverse datasets from recent literature (e.g., biological data and object recognition, among others), and compared against filter, embedded and wrappers methods. The results are remarkable in terms of accuracy, stability and low execution time.Comment: Preprint version - Lecture Notes in Computer Science - Springer 201

Online Feature Selection for Visual Tracking

Object tracking is one of the most important tasks in many applications of computer vision. Many tracking methods use a fixed set of features ignoring that appearance of a target object may change drastically due to intrinsic and extrinsic factors. The ability to dynamically identify discriminative features would help in handling the appearance variability by improving tracking performance. The contribution of this work is threefold. Firstly, this paper presents a collection of several modern feature selection approaches selected among filter, embedded, and wrapper methods. Secondly, we provide extensive tests regarding the classification task intended to explore the strengths and weaknesses of the proposed methods with the goal to identify the right candidates for online tracking. Finally, we show how feature selection mechanisms can be successfully employed for ranking the features used by a tracking system, maintaining high frame rates. In particular, feature selection mounted on the Adaptive Color Tracking (ACT) system operates at over 110 FPS. This work demonstrates the importance of feature selection in online and realtime applications, resulted in what is clearly a very impressive performance, our solutions improve by 3% up to 7% the baseline ACT while providing superior results compared to 29 state-of-the-art tracking methods

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

Localized Manifold Harmonics for Spectral Shape Analysis

The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer graphics and geometry processing applications. In particular, Laplacian eigenbases allow generalizing the classical Fourier analysis to manifolds. A key drawback of such bases is their inherently global nature, as the Laplacian eigenfunctions carry geometric and topological structure of the entire manifold. In this paper, we introduce a new framework for local spectral shape analysis. We show how to efficiently construct localized orthogonal bases by solving an optimization problem that in turn can be posed as the eigendecomposition of a new operator obtained by a modification of the standard Laplacian. We study the theoretical and computational aspects of the proposed framework and showcase our new construction on the classical problems of shape approximation and correspondence. We obtain significant improvement compared to classical Laplacian eigenbases as well as other alternatives for constructing localized bases

Sub-Laplacian with drift in nilpotent Lie groups

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«Cher Paul Morand…»: quel filo sottile fra "Venises" di Paul Morand e "Le Labyrinthe du Monde" di Marguerite Yourcenar

The article presents some analogies between Marguerite Yourcenar and Paul Morand, both attracted by the decadent beauty of Venice, which the two intellectuals consider as the symbol of the whole agonising Europe. Bounded by a subtle and resilient thread, the two authors evade from a disappointing and stifling present evoking an imperfect past which allows them to give sense to life

Su alcune varianti nell'Oeuvre au Noir di M. Yourcenar

В молодости Маргерит Юрсенар написала роман D’aprиs Dürer (1933-1934 гг.), в котором изобразила историю Зенона, еврейского философа, врача, алхимика XVI-го века. В 1950-е годы Юрсенар неоднократно возвращалась к D’aprиs Dürer, перерабатывая текст, пока в 1968 году он не принял окончательной формы Oeuvre au Noir. Настоящее исследование касается ряда примеров из I-ой и IX-глав Oeuvre au Noir, которые содержат варианты из D’aprиs Dürer. Исследование основывается на сопоставлении рукописи D’aprиs Dürer, хранящейся в Hougton Library (Cambridge, Mass.) и издания Oeuvre au Noir 1968-г. Сопоставив окончательную редакцию с романом 1933-34 года, автор приходит к выводу, что изменения, внесенные в результате 30-летнего вынашивания замысла (замена, исключение, добавление отдельных слов), иногда приводят к радикальной трансформации смысла текста.Università degli Studi di Trieste - Scuola Superiore di Lingue Moderne per Interpreti e Traduttori. Università degli Studi di Bergamo - Dipartimento di Linguistica e Letterature comparat