Publication

Nmf-Based Blind Source Separation Using A Linear Predictive Coding Error Clustering Criterion

Related publications (37)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Robust and Hierarchical Stop Discovery in Sparse and Diverse Trajectories

Quoc Viet Hung Nguyen, Zhixian Yan, Le Hung Tran, Ngoc Hoan Do

The advance of GPS tracking technique brings a large amount of trajectory data. To better understand such mobility data, semantic models like “stop/move” (or inferring “activity”, “transportation mode”) recently become a hot topic for trajectory data analy ...

2011

Appearance-based Keypoint Clustering

Pascal Fua, Sabine Süsstrunk, Vincent Lepetit, Francisco Estrada

We present an algorithm for clustering sets of detected interest points into groups that correspond to visually distinct structure. Through the use of a suitable colour and texture representation, our clustering method is able to identify keypoints that be ...

2009

Fast high-dimensional Bayesian classification and clustering

Vahid Partovi Nia

We introduce a fast approach to classification and clustering applicable to high-dimensional continuous data, based on Bayesian mixture models for which explicit computations are available. This permits us to treat classification and clustering in a single ...

EPFL2009

A Clustering Method Based on Soft Learning of Model (Prototype) and Dissimilarity Metrics

Arash Arami

Many clustering methods are designed for especial cluster types or have good performance dealing with particular size and shape of clusters. The main problem in this connection is how to define a similarity (or dissimilarity) criterion to make an algorithm ...

Springer-Verlag2009

Learning Cluster Type and Dissimilarity Metric for Each Cluster Using a Set of Possible Cluster Types

Arash Arami

One of the shortcomings of the existing clustering methods is their problems dealing with different shape and size clusters. On the other hand, most of these methods are designed for especial cluster types or have good performance dealing with particular s ...

2007

Counting pedestrians in video sequences using trajectory clustering

Jean-Philippe Thiran, Gianluca Antonini

In this paper we propose to use clustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/ tracking systems that overestimate the number of targets. Clustering techniques are applied to ...

2006

Kernelized Infomax Clustering

We propose a simple information-theoretic clustering approach based on maximizing the mutual information

I(\sfx,y)

between the unknown cluster labels

y

and the training patterns

\sfx

with respect to parameters of specifically constrained encoding dis ...

IDIAP2005

Noisy Text Clustering

David Grangier, Alessandro Vinciarelli

This work presents document clustering experiments performed over noisy texts (i.e. text that have been extracted through an automatic process like speech or character recognition). The effect of recognition errors on different clustering techniques is mea ...

IDIAP2004

Analysis of a Synonymy Network

Paolo De Los Rios, Jean-Cédric Chappelier, David Gfeller

The purpose of this report is to present the study of a linguistic network based on the relation of synonymy. The network has been extracted from a dictionary of synonyms in French. Due to errors and ambiguities in the data, the synonyms are not always gro ...

2004

A Robust Speaker Clustering Algorithm

Jitendra Ajmera

In this paper, we present a novel speaker segmentation and clustering algorithm. The algorithm automatically performs both speaker segmentation and clustering without any prior knowledge of the identities or the number of speakers. Advantages of this algor ...

IDIAP2003