Juan C. Alvarado-Pérez

Bridging the gap between human knowledge and machine learning

Nowadays, great amount of data is being created by several sources from academic, scientific, business and industrial activities. Such data intrinsically contains meaningful information allowing for developing techniques, and have scientific validity to explore the information thereof. In this connection, the aim of artificial intelligence (AI) is getting new knowledge to make decisions properly. AI has taken an important place in scientific and technology development communities, and recently develops computer-based processing devices for modern machines. Under the premise, the premise that the feedback provided by human reasoning -which is holistic, flexible and parallel- may enhance the data analysis, the need for the integration of natural and artificial intelligence has emerged. Such an integration makes the process of knowledge discovery more effective, providing the ability to easily find hidden trends and patterns belonging to the database predictive model. As well, allowing for new observations and considerations from beforehand known data by using both data analysis methods and knowledge and skills from human reasoning. In this work, we review main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this emergent field. As well, key aspects to conceptually compare them are provided.

Interactive interface for efficient data visualization via a geometric approach

Dimensionality reduction (DR) methods represent a suitable alternative to visualizing data. Nonetheless, most of them still lack the properties of interactivity and controllability. In this work, we propose a data visualization interface that allows for user interaction within an interactive framework. Specifically, our interface is based on a mathematic geometric model, which combines DR methods through a weighted sum. Interactivity is provided in the sense that weighting factors are given by the user via the selection of points inside a geometric surface. Then, (even non-expert) users can intuitively either select a concrete DR method or carry out a mixture of methods. Experimental results are obtained using artificial and real datasets, demonstrating the usability and applicability of our interface in DR-based data visualization.

Interactive Data Visualization Using Dimensionality Reduction and Dissimilarity-Based Representations

This work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is carried out by a weighted sum. For the sake of user interaction, corresponding weighting factors are given via an intuitive color-based interface. Also, to depict the DR outcomes while showing information about the input high-dimensional data space, the low-dimensional representations reached by the mixture is conveyed using scatter plots enhanced with an interactive data-driven visualization. In this connection, a constrained dissimilarity approach define the graph to be drawn on the scatter plot.

Interactive visualization methodology of high-dimensional data with a color-based model for dimensionality reduction

Nowadays, a consequence of data overload is that worlds technology capacity to collect, communicate, and store large volumes of data is increasing faster than human analysis skills. Such an issue has motivated the development of graphic ways to visually represent and analyze high-dimensional data. Particularly, in this work, we propose a graphical interface that allow the combination of dimensionality reduction (DR) methods using a chromatic model to make data visualization more intelligible for humans. This interface is designed for an easy and interactive use, so that input parameters are given by the user via the selection of RGB values inside a given surface. Proposed interface enables (even non-expert) users to intuitively either select a concrete DR method or carry out a mixture of methods. Experimental results proves the usability of our interface making the selection or configuration of a DR-based visualization an intuitive and interactive task for the user.

Multiple Kernel Learning for Spectral Dimensionality Reduction

This work introduces a multiple kernel learning (MKL) approach for selecting and combining different spectral methods of dimensionality reduction (DR). From a predefined set of kernels representing conventional spectral DR methods, a generalized kernel is calculated by means of a linear combination of kernel matrices. Coefficients are estimated via a variable ranking aimed at quantifying how much each variable contributes to optimize a variance preservation criterion. All considered kernels are tested within a kernel PCA framework. The experiments are carried out over well-known real and artificial data sets. The performance of compared DR approaches is quantified by a scaled version of the average agreement rate between K-ary neighborhoods. Proposed MKL approach exploits the representation ability of every single method to reach a better embedded data for both getting more intelligible visualization and preserving the structure of data.

Artificial and Natural Intelligence Integration

The large amount of data generated by different activities -academic, scientific, business and industrial activities, among others- contains meaningful information that allows developing processes and techniques, which have scientific validity to optimally explore such information. Doing so, we get new knowledge to properly make decisions. Nowadays a new and innovative field is rapidly growing in importance that is Artificial Intelligence, which involves computer processing devices of modern machines and human reasoning. By synergistically combining them –in other words, performing an integration of natural and artificial intelligence-, it is possible to discover knowledge in a more effective way in order to find hidden trends and patterns belonging to the predictive model database. As well, allowing for new observations and considerations from beforehand known data by using data analysis methods as well as the knowledge and skills (of holistic, flexible and parallel type) from human reasoning. This work briefly reviews main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this field integration approaches. As well, key aspects to conceptually compare them are provided.

Interactive Data Visualization Using Dimensionality Reduction and Similarity-Based Representations

This work presents a new interactive data visualization approach based on mixture of the outcomes of dimensionality reduction (DR) methods. Such a mixture is a weighted sum, whose weighting factors are defined by the user through a visual and intuitive interface. Additionally, the low-dimensional representation space produced by DR methods are graphically depicted using scatter plots powered via an interactive data-driven visualization. To do so, pairwise similarities are calculated and employed to define the graph to be drawn on the scatter plot. Our visualization approach enables the user to interactively combine DR methods while provided information about the structure of original data, making then the selection of a DR scheme more intuitive.

On the Spectral Clustering for Dynamic Data

Spectral clustering has shown to be a powerful technique for grouping and/or rank data as well as a proper alternative for unlabeled problems. Particularly, it is a suitable alternative when dealing with pattern recognition problems involving highly hardly separable classes. Due to its versatility, applicability and feasibility, this clustering technique results appealing for many applications. Nevertheless, conventional spectral clustering approaches lack the ability to process dynamic or time-varying data. Within a spectral framework, this work presents an overview of clustering techniques as well as their extensions to dynamic data analysis.

On the Relationship Between Dimensionality Reduction and Spectral Clustering from a Kernel Viewpoint

This paper presents the development of a unified view of spectral clustering and unsupervised dimensionality reduction approaches within a generalized kernel framework. To do so, the authors propose a multipurpose latent variable model in terms of a high-dimensional representation of the input data matrix, which is incorporated into a least-squares support vector machine to yield a generalized optimization problem. After solving it via a primal-dual procedure, the final model results in a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Specifically, our formulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis.

Dimensionality reduction for interactive data visualization via a Geo-Desic approach

This work presents a dimensionality reduction (DR) framework that enables users to perform either the selection or mixture of DR methods by means of an interactive model, here named Geo-Desic approach. Such a model consists of linear combination of kernel-based representations of DR methods, wherein the corresponding coefficients are related to coordinated latitude and longitude inside of the world map. By incorporating the Geo-Desic approach within an interface, the combination may be made easily and intuitively by users -even non-expert ones- fulfilling their criteria and needs, by just picking up points from the map. Experimental results demonstrates the usability and ability of DR methods representation of proposed approach.