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Analysing the temporal dynamics of model performance for hydrological models

Related publications (44)

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Robust Discriminative Clustering with Sparse Regularizers

Nicolas Henri Bernard Flammarion

Clustering high-dimensional data often requires some form of dimensionality reduction, where clustered variables are separated from "noise-looking" variables. We cast this problem as finding a low-dimensional projection of the data which is well-clustered. ...

2017

Graph-based Methods for Visualization and Clustering

Johann Paratte

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017

Temporally Coherent Clustering of Student Data

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show l ...

EDM2016

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Typical Industrial Cluster - Chemistry/refinery/petrochemistry

Laurent Gabriel Félix Alexandre Stéphane Bungener

A refinery and petrochemical site are described in the document. Data for the steam consumption and production of the cluster's process units are also described. The cluster can be broken down into two individual sites made up of process units and process ...

2016

Structure Modeling of High Dimensional Data

Mahdad Hosseini Kamal

The digitization of our most common appliances has led to a literal data deluge, some- times referred to as Big Data. The ever increasing volume of data we generate, coupled with our desire to exploit it ever faster, forces us to come up with innovative da ...

EPFL2015

Discovering places of interest in everyday life from smartphone data

Daniel Gatica-Perez

In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users’ real lives. A place-of-interest is defined as a location where ...

Springer2013

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

Low-rank Matrix Approximation Using Point-wise Operators

Arash Amini, Amin Karbasi

Extracting low dimensional structure from high dimensional data arises in many applications such as machine learning, statistical pattern recognition, wireless sensor networks, and data compression. If the data is restricted to a lower dimensional subspace ...

Institute of Electrical and Electronics Engineers2012

Application of Subspace Gaussian Mixture Models in Contrastive Acoustic Scenarios

Petr Motlicek, Philip Neil Garner, Fabio Valente, David Imseng

This paper describes experimental results of applying Subspace Gaussian Mixture Models (SGMMs) in two completely diverse acoustic scenarios: (a) for Large Vocabulary Continuous Speech Recognition (LVCSR) task over (well-resourced) English meeting data and, ...

Idiap2012