Ahlame Douzal-Chouakria

Generalized k-means-based clustering for temporal data under weighted and kernel time warp

Generalize k-means-based clustering to temporal data under time warp.Extend time warp measures and temporal kernels to capture local temporal differences.Propose a tractable estimation of the cluster representatives under extended measures.Propose fast solutions that capture both global and local temporal features.Deep analysis on a wide range of 20 non-isotropic, linearly non-separable public data. Temporal data naturally arise in various emerging applications, such as sensor networks, human mobility or internet of things. Clustering is an important task, usually applied a priori to pattern analysis tasks, for summarization, group and prototype extraction; it is all the more crucial for dimensionality reduction in a big data context. Clustering temporal data under time warp measures is challenging because it requires aligning multiple temporal data simultaneously. To circumvent this problem, costly k-medoids and kernel k-means algorithms are generally used. This work investigates a different approach to temporal data clustering through weighted and kernel time warp measures and a tractable and fast estimation of the representative of the clusters that captures both global and local temporal features. A wide range of 20 public and challenging datasets, encompassing images, traces and ecg data that are non-isotropic (i.e., non-spherical), not well-isolated and linearly non-separable, is used to evaluate the efficiency of the proposed temporal data clustering. The results of this comparison illustrate the benefits of the method proposed, which outperforms the baselines on all datasets. A deep analysis is conducted to study the impact of the data specifications on the effectiveness of the studied clustering methods.

Multi-modal and multi-scale temporal metric learning for a robust time series nearest neighbors classification

Abstract The definition of a metric between time series is inherent to several data analysis and mining tasks, including clustering, classification or forecasting. Time series data present naturally several modalities covering their amplitude, behavior or frequential spectrum, that may be expressed with varying delays and at multiple temporal scales—exhibited globally or locally. Combining several modalities at multiple temporal scales to learn a holistic metric is a key challenge for many real temporal data applications. This paper proposes a Multi-modal and Multi-scale Temporal Metric Learning ( m 2 tml ) approach for a robust time series nearest neighbors classification. The solution lies in embedding time series into a dissimilarity space where a pairwise svm is used to learn both linear and non linear combined metric. A sparse and interpretable variant of the solution shows the ability of the learned temporal metric to localize accurately discriminative modalities as well as their temporal scales. A wide range of 30 public and challenging datasets, encompassing images, traces and ecg data, are used to show the efficiency and the potential of m 2 tml for an effective time series nearest neighbors classification.

A Multi-modal Metric Learning Framework for Time Series kNN Classification

This work proposes a temporal and frequential metric learning framework for a time series nearest neighbor classification. For that, time series are embedded into a pairwise space where a combination function is learned based on a maximum margin optimization process. A wide range of experiments are conducted to evaluate the ability of the learned metric on time series kNN classification.

Time warp invariant k SVD: Sparse coding and dictionary learning for time series under time warp

Abstract Learning dictionary for sparse representing time series is an important issue to extract latent temporal features, reveal salient primitives and sparsely represent complex temporal data. Time series are challenging data, they are often of different durations, may be composed of local or global salient events, that may arise with varying delays at different time stamps. This paper addresses the sparse coding and dictionary learning for such challenging time series. For that, we propose a non linear time warp invariant kSVD ( twi -k svd ) where both input samples and dictionary atoms may have different lengths while involving varying delays. For the sparse coding problem, we propose an efficient time warp invariant orthogonal matching pursuit based on a new cosine maximisation time warp operator and induced sibling atoms. For the dictionary learning, thanks to a rotation transformation between each atom and its sibling atoms, a singular value decomposition is used to jointly approximate the coefficients and update the dictionary. The proposed method is confronted to major shift invariant, convolved and kernel dictionary learning methods on several challenging character and digit handwritten trajectories. The experiments conducted show the potential of twi -k svd to efficiently sparse represent time series and to extract latent discriminative primitives for time series classification.

A large margin time series nearest neighbour classification under locally weighted time warps

Accuracy of the k-nearest neighbour (

Multiple metric learning for large margin kNN classification of time series

k\hbox {NN}

Prédictions d'activité dans les réseaux sociaux en ligne

kNN) classifier depends strongly on the ability of the used distance to induce k-nearest neighbours of the same class while keeping distant samples of different classes. For time series classification,