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Smaller, Faster & Lighter KNN Graph Constructions

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RUBIK: Efficient Threshold Queries on Massive Time Series

Anastasia Ailamaki, Thomas Heinis, Farhan Tauheed, Eleni Tzirita Zacharatou

An increasing number of applications from finance, meteorology, science and others are producing time series as output. The analysis of the vast amount of time series is key to understand the phenomena studied, particularly in the simulation sciences, wher ...

2015

Sparse Binary Features for Image Classification

Johann Paratte

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...

2013

Nonlinear data description with Principal Polynomial Analysis

Devis Tuia

Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold lear ...

2012

Prony Series Representation for the Lightning Channel Base Current

In this paper, a new representation for the lightning channel base current is proposed in terms of the so-called Prony series, obtained by means of a modified time-domain variant of the well-known vector fitting technique. This method allows both to reprod ...

2012

Beyond Graphs: A New Synthesis

Dario Floreano, Claudio Mattiussi, Daniel Marbach, Peter Dürr

Artiﬁcial neural networks, electronic circuits, and gene networks are some examples of systems that can be modeled as networks, that is, as collections of interconnected nodes. In this paper we introduce the concept of terminal graph (t-graph for short), w ...

2011

Subspace Gaussian Mixture Models for speech recognition

Pinar Akyazi, Samuel Thomas

We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM) ...

2010

Distributed Similarity Search in High Dimensions Using Locality Sensitive Hashing

Karl Aberer, Sebastian Michel, Parisa Haghani

In this paper we consider distributed K-Nearest Neighbor (KNN) search and range query processing in high dimensional data. Our approach is based on Locality Sensitive Hashing (LSH) which has proven very efficient in answering KNN queries in centralized set ...

2009

LSH At Large - Distributed KNN Search in High Dimensions

Karl Aberer, Sebastian Michel, Parisa Haghani

We consider K-Nearest Neighbor search for high dimensional data in large-scale structured Peer-to-Peer networks. We present an efficient mapping scheme based on p-stable Locality Sensitive Hashing to assign hash buckets to peers in a Chord-style overlay ne ...

2008

Partial and approximate symmetry detection for 3D geometry

Mark Pauly

"Symmetry is a complexity-reducing concept [...]; seek it every-where." - Alan J. Perlis Many natural and man-made objects exhibit significant symmetries or contain repeated substructures. This paper presents a new algorithm that processes geometric models ...

2006

On canonical representation of convex polyhedra

Stefano Picozzi

Convex polyhedra are important objects in various areas of mathematics and other disciplines. A fundamental result, known as Minkowski-Weyl theorem, states that every polyhedron admits two types of representations, either as the solution set to a finite sy ...

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