Search results for "Filtering"

Bootstrap validation of links of a minimum spanning tree

2018

We describe two different bootstrap methods applied to the detection of a minimum spanning tree obtained from a set of multivariate variables. We show that two different bootstrap procedures provide partly distinct information that can be highly informative about the investigated complex system. Our case study, based on the investigation of daily returns of a portfolio of stocks traded in the US equity markets, shows the degree of robustness and completeness of the information extracted with popular information filtering methods such as the minimum spanning tree and the planar maximally filtered graph. The first method performs a "row bootstrap" whereas the second method performs a "pair bo…

Filtering with dissipativity for T-S fuzzy systems with time-varying delay: Reciprocally convex approach

2013

This paper is focused on the problem of reliable filter design with strictly dissipativity for a class of discrete-time T-S fuzzy time-delay systems. Our attention is paid on the design of reliable filter to ensure a strictly dissipative performance for the filtering error system. By employing the reciprocally convex approach, a sufficient condition of dissipativity analysis is obtained for T-S fuzzy delayed systems with sensor failures. A desired reliable filter is designed by solving a convex optimization problem.

Propriétés spectrales des filtres usuels en économie : désaisonnalisation par CENSUS XII, et différences d'ordre d

1985

This study concerns the efficiency of usual statistical filters in economics to reduce the tendencial and seasonal components : the moving average processes of CENSUS Xll seasonal adjustment method and the difference sof d order early proposed by BOX-JENKINS. We are herein using the spectral and cospectral analyses to know whether the other components keep stable in the filtering process and whether the leads and lags found on the basis of the initial original data are kept after these filtering processes. One application illustrates the problems by means of monthly indicators that are highly representative of French economic evolution from 1963 up to 1982.This theoretical and empirical res…

Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

2017

Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. Ac…

An Indoor and Outdoor Navigation System for Visually Impaired People

2019

In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in r…

2020

Recommender systems are information software that retrieves relevant items for users from massive sources of data. The variational autoencoder (VAE) has proven to be a promising approach for recommendation systems, as it can explore high-level user-item relations and extract contingencies from the input effectively. However, the previous variants of VAE have so far seen limited application to domain-specific recommendations that require additional side information. Hence, The Ensemble Variational Autoencoder framework for recommendations (EnsVAE) is proposed. This architecture specifies a procedure to transform sub-recommenders’ predicted utility matrix into interest probabilities that allo…

Filtering Real World Networks: A Correlation Analysis of Statistical Backbone Techniques

2023

Networks are an invaluable tool for representing and understanding complex systems. They offer a wide range of applications, including identifying crucial nodes, uncovering communities, and exploring network formation. However, when dealing with large networks, the computational challenge can be overwhelming. Fortunately, researchers have developed several techniques to address this issue by reducing network size while preserving its fundamental properties [1-9]. To achieve this goal, two main approaches have emerged: structural and statistical methods. Structural methods aim to keep a set of topological features of the network while reducing its size. In contrast, statistical methods elimi…

NetBone: A Python Package for Extracting Backbones of Weighted Networks

2023

NetBone is a new open-source Python package designed to simplify analyzing complex networks. With a wide range of techniques available, Net-Bone allows researchers to extract the backbone of a network while preserving its essential structure. The package includes nine structural methods and five statistical techniques, offering users a comprehensive solution to network analysis. It is user-friendly and straightforward to use, with easy installation. The package accepts different types of inputs, including data frames or Networkx graphs, and provides evaluation measures for comparative purposes. Additionally, NetBone offers an option to generate plots. Its versatility makes it a valuable too…

The ATLAS Data Acquisition and High Level Trigger system

2016

Journal of Instrumentation 11(06), P06008 (2016). doi:10.1088/1748-0221/11/06/P06008

Improving point matching on multimodal images using distance and orientation automatic filtering

2016

International audience; Speed Up Robust Features SURF is one of the most popular and efficient methods used for image registration task. In order to achieve a correct registration, a good matching of feature point is required. However in the case of multimodal images, the high and non-linear intensity changes between different modalities led to many outliers (mismatching of detected points) and consequently a fail in the registration. Therefore, in this paper we introduce an efficient method devoted to the detection and removal of such outlier. It's based on an automatic filtering of outliers on both distance and orientation between features points. We tested our proposed method on a set of…