Type 1 and 2 mixtures of Kullback–Leibler divergences as cost functions in dimensionality reduction based on similarity preservation

Lee, John A.; Renard, Emilie; Bernard, Guillaume; Dupont, Pierre; Verleysen, Michel

doi:10.1016/j.neucom.2012.12.036

Cited by 79 publications

(66 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Further options are to let the analyst determine interesting features in combination with subspace clustering (e.g., [41]) or quality metrics (e.g., [31]). S4 Feature Selection & Emphasis was the most frequently implemented interaction scenario (37). S5 DR Parameter Tuning: Some DR algorithms contain specific parameters that can be tuned, such as LDA regularization in [13].…”

Section: S4 Feature Selection and Emphasismentioning

confidence: 99%

“…Observations: The final result of our coding process is shown in Table 1 and Figure 4. To provide an overview of the coded results, we created a 2D projection of the papers using Multiscale Jensen-Shannon Embedding [37], which aims to place papers with similar codes nearby in the projection. Together with Table 1 we can investigate combinations of interaction scenarios.…”

Section: S6 Defining Constraintsmentioning

confidence: 99%

“…On the algorithmic side, the challenge is to formulate specific DR algorithms as parametric instances that allow smooth transitions between different DR types. For example, continuous model spaces [37,55] enable analysts to track and interpret model switching and avoid abrupt and confusing transitions.…”

Section: Generative Use Of the Process Model -Opportunitiesmentioning

confidence: 99%

See 2 more Smart Citations

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Sacha

Zhang

Sedlmair

et al. 2017

IEEE Trans. Visual. Comput. Graphics

Self Cite

202

139

View full text Add to dashboard Cite

Abstract-Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a "human in the loop" process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

show abstract

Section: S4 Feature Selection and Emphasismentioning

confidence: 99%

Section: S6 Defining Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Sacha

Zhang

Sedlmair

et al. 2017

IEEE Trans. Visual. Comput. Graphics

Self Cite

202

139

View full text Add to dashboard Cite

show abstract

“…Here, the range is Q. NX The last one, R NX (K) [60], can be considered a renormalized Q^, allowing us to compare values at different scales. R NX (K) is based on Q_ k with a baseline subtraction and a normalization: it indicates the relative improvement in a random embedding.…”

Section: T)e E «U)-fc) (6)mentioning

confidence: 99%

A methodology to compare Dimensionality Reduction algorithms in terms of loss of quality

Gracia

González

Robles

et al. 2014

Information Sciences

View full text Add to dashboard Cite

Dimensionality Reduction (DR) is attracting more attention these days as a result of the increasing need to handle huge amounts of data effectively. DR methods allow the number of initial features to be reduced considerably until a set of them is found that allows the original properties of the data to be kept. However, their use entails an inherent loss of quality that is likely to affect the understanding of the data, in terms of data analysis. This loss of quality could be determinant when selecting a DR method, because of the nature of each method.In this paper, we propose a methodology that allows different DR methods to be analyzed and compared as regards the loss of quality produced by them. This methodology makes use of the concept of preservation of geometry (quality assessment criteria) to assess the loss of quality. Experiments have been carried out by using the most well-known DR algorithms and quality assessment criteria, based on the literature. These experiments have been applied on 12 real-world datasets.Results obtained so far show that it is possible to establish a method to select the most appropriate DR method, in terms of minimum loss of quality. Experiments have also highlighted some interesting relationships between the quality assessment criteria. Finally, the methodology allows the appropriate choice of dimensionality for reducing data to be established, whilst giving rise to a minimum loss of quality.

show abstract

“…Genuine similarity preservation, with similarities in both HD and LD spaces, appeared later with stochastic neighbour embedding [12] (SNE). Interest in this new paradigm grew after the publication of variants such as t-distributed SNE (t-SNE) [9], neighbourhood retrieval and visualisation (NeRV) [13], and Jensen-Shannon embedding (JSE) [14]. These methods significantly outperformed older ones in terms of DR quality, especially when it comes to render accurately small-size neighbourhoods around each datum.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure

Lee

Peluffo-Ordóñez²,

Verleysen³

2015

Neurocomputing

Self Cite

View full text Add to dashboard Cite

Type 1 and 2 mixtures of Kullback–Leibler divergences as cost functions in dimensionality reduction based on similarity preservation

Cited by 79 publications

References 29 publications

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

A methodology to compare Dimensionality Reduction algorithms in terms of loss of quality

Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure

Contact Info

Product

Resources

About