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      <div class="TdmEntry">Overall Objectives<ul><li><a href="./uid3.html">Context</a></li><li><a href="./uid4.html">Goals</a></li></ul></div>
      <div class="TdmEntry">Research Program<ul><li><a href="uid6.html&#10;&#9;&#9;  ">Research axis 1: Unsupervised learning</a></li><li><a href="uid7.html&#10;&#9;&#9;  ">Research axis 2: Performance assessment</a></li><li><a href="uid8.html&#10;&#9;&#9;  ">Research axis 3: Functional data</a></li><li><a href="uid9.html&#10;&#9;&#9;  ">Research axis 4: Applications motivating research</a></li></ul></div>
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      <div class="TdmEntry">New Software and Platforms<ul><li><a href="uid20.html&#10;&#9;&#9;  ">MixtComp</a></li><li><a href="uid24.html&#10;&#9;&#9;  ">BlockCluster</a></li><li class="tdmActPage"><a href="uid29.html&#10;&#9;&#9;  ">CloHe</a></li><li><a href="uid33.html&#10;&#9;&#9;  ">PACBayesianNMF</a></li><li><a href="uid37.html&#10;&#9;&#9;  ">pycobra</a></li><li><a href="uid42.html&#10;&#9;&#9;  ">STK++</a></li><li><a href="uid46.html&#10;&#9;&#9;  ">rtkore</a></li><li><a href="uid50.html&#10;&#9;&#9;  ">MixAll</a></li><li><a href="uid55.html&#10;&#9;&#9;  ">simerge</a></li><li><a href="uid58.html&#10;&#9;&#9;  ">MixtComp.V4</a></li><li><a href="uid62.html&#10;&#9;&#9;  ">MASSICCC</a></li><li><a href="uid65.html&#10;&#9;&#9;  ">Platforms</a></li></ul></div>
      <div class="TdmEntry">New Results<ul><li><a href="uid68.html&#10;&#9;&#9;  ">Axis 1: Data Units Selection in Statistics</a></li><li><a href="uid69.html&#10;&#9;&#9;  ">Axis 1: Model-Based Co-clustering for Ordinal Data of different dimensions</a></li><li><a href="uid70.html&#10;&#9;&#9;  ">Axis 1: Model-based co-clustering for mixed type data</a></li><li><a href="uid71.html&#10;&#9;&#9;  ">Axis 1: Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data</a></li><li><a href="uid72.html&#10;&#9;&#9;  ">Axis 1: Gaussian-based visualization of Gaussian and non-Gaussian model-based clustering</a></li><li><a href="uid73.html&#10;&#9;&#9;  ">Axis 1: Co-clustering: A versatile way to perform clustering</a></li><li><a href="uid74.html&#10;&#9;&#9;  ">Axis 1: Dealing with missing data in model-based clustering through a MNAR model</a></li><li><a href="uid75.html&#10;&#9;&#9;  ">Axis 1: Organized Co-Clustering for textual data synthesis</a></li><li><a href="uid76.html&#10;&#9;&#9;  ">Axis 1: Model-Based Co-clustering with Co-variables</a></li><li><a href="uid77.html&#10;&#9;&#9;  ">Axis 1: Linking canonical and spectral clustering</a></li><li><a href="uid78.html&#10;&#9;&#9;  ">Axis 1: Predictive clustering</a></li><li><a href="uid79.html&#10;&#9;&#9;  ">Axis 1: Ranking and synchronization from pairwise measurements via SVD</a></li><li><a href="uid80.html&#10;&#9;&#9;  ">Axis 1: SPONGE: A generalized eigenproblem for clustering signed networks</a></li><li><a href="uid81.html&#10;&#9;&#9;  ">Axis 2: Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method</a></li><li><a href="uid82.html&#10;&#9;&#9;  ">Axis 2: Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping</a></li><li><a href="uid83.html&#10;&#9;&#9;  ">Axis 2: Learning general sparse additive models from point queries in high dimensions</a></li><li><a href="uid84.html&#10;&#9;&#9;  ">Axis 2: Sparse non-negative super-resolution - simplified and stabilized </a></li><li><a href="uid85.html&#10;&#9;&#9;  ">Axis 2: Pseudo-Bayesian learning with kernel Fourier transform as prior</a></li><li><a href="uid86.html&#10;&#9;&#9;  ">Axis 2: PAC-Bayesian binary activated deep neural networks</a></li><li><a href="uid87.html&#10;&#9;&#9;  ">Axis 2: Improved PAC-Bayesian Bounds for Linear Regression</a></li><li><a href="uid88.html&#10;&#9;&#9;  ">Axis 2: Multiview Boosting by controlling the diversity and the accuracy of view-specific voters</a></li><li><a href="uid89.html&#10;&#9;&#9;  ">Axis 2: PAC-Bayes and Domain Adaptation</a></li><li><a href="uid90.html&#10;&#9;&#9;  ">Axis 2: Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory</a></li><li><a href="uid91.html&#10;&#9;&#9;  ">Axis 2: Still no free lunches: the price to pay for tighter PAC-Bayes bounds</a></li><li><a href="uid92.html&#10;&#9;&#9;  ">Axis 2: PAC-Bayesian Contrastive Unsupervised Representation Learning</a></li><li><a href="uid93.html&#10;&#9;&#9;  ">Axis 2: Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly</a></li><li><a href="uid94.html&#10;&#9;&#9;  ">Axis 2: PAC-Bayes Un-Expected Bernstein Inequality</a></li><li><a href="uid95.html&#10;&#9;&#9;  ">Axis 2: Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria</a></li><li><a href="uid96.html&#10;&#9;&#9;  ">Axis 2: Revisiting clustering as matrix factorisation on the Stiefel manifold</a></li><li><a href="uid97.html&#10;&#9;&#9;  ">Axis 2: A Primer on PAC-Bayesian Learning</a></li><li><a href="uid98.html&#10;&#9;&#9;  ">Axis 2: Perturbed Model Validation: A New Framework to Validate Model Relevance</a></li><li><a href="uid99.html&#10;&#9;&#9;  ">Axis 2: Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles</a></li><li><a href="uid100.html&#10;&#9;&#9;  ">Axis 2: Online k-means Clustering</a></li><li><a href="uid101.html&#10;&#9;&#9;  ">Axis 2: Non-linear aggregation of filters to improve image denoising</a></li><li><a href="uid102.html&#10;&#9;&#9;  ">Axis 2: Multiple change-points detection with reproducing kernels</a></li><li><a href="uid103.html&#10;&#9;&#9;  ">Axis 2: Analysis of early stopping rules based on discrepancy principle</a></li><li><a href="uid104.html&#10;&#9;&#9;  ">Axis 3: Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models</a></li><li><a href="uid105.html&#10;&#9;&#9;  ">Axis 3: Mathematical Modeling and Study of Random or Deterministic Phenomena</a></li><li><a href="uid106.html&#10;&#9;&#9;  ">Axis 3: Categorical functional data analysis</a></li><li><a href="uid107.html&#10;&#9;&#9;  ">Axis 4: Proteomic signature of early death in heart failure patients</a></li><li><a href="uid108.html&#10;&#9;&#9;  ">Axis 4: Statistical analysis of high-throughput proteomic data</a></li><li><a href="uid109.html&#10;&#9;&#9;  ">Axis 4: Linking different kinds of Omics data through a model-based clustering approach</a></li><li><a href="uid110.html&#10;&#9;&#9;  ">Axis 4: Real-time Audio Sources Classification</a></li><li><a href="uid111.html&#10;&#9;&#9;  ">Axis 4: Matching of descriptors evolving over time</a></li><li><a href="uid112.html&#10;&#9;&#9;  ">Axis 4: Supervised multivariate discretization and levels merging for logistic regression</a></li><li><a href="uid113.html&#10;&#9;&#9;  ">Axis 4: MASSICCC Platform for SaaS Software Availability</a></li><li><a href="uid114.html&#10;&#9;&#9;  ">Axis 4: Domain adaptation from a pre-trained source model</a></li><li><a href="uid115.html&#10;&#9;&#9;  ">Axis 4: Reject Inference Methods in Credit Scoring: a rational review</a></li><li><a href="uid116.html&#10;&#9;&#9;  ">Other: Projection Under Pairwise Control</a></li></ul></div>
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	    Raweb 
	    2019</a> | <a href="http://www.inria.fr/en/teams/modal">Presentation of the Project-Team MODAL</a></small>
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        <h2>Section: 
      New Software and Platforms</h2>
        <h3 class="titre3">CloHe</h3>
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          <i>Clustering of Mixed data</i>
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        <p><span class="smallcap">Keywords: </span> Classification - Clustering - Missing data</p>
        <p><span class="smallcap">Functional Description: </span> Software of classification for mixed data with missing values with application to multispectral satellite image time-series</p>
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            <p class="notaparagraph"><a name="uid30"> </a>Partners: CNRS - INRA</p>
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            <p class="notaparagraph"><a name="uid31"> </a>Contact: Serge Iovleff</p>
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            <p class="notaparagraph"><a name="uid32"> </a>URL: <a href="https://modal.lille.inria.fr/CloHe/">https://modal.lille.inria.fr/CloHe/</a></p>
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