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Dimensionality Reduction (DR) techniques are widely used to analyze and make sense of high-dimensional data. Each method is geared towards preserving a different aspect of the data. For example, some techniques favor neighborhood preservation whereas others favor distance preservation. While these DR techniques help users to represent their data, it makes a complex task to select a suitable DR. Also, most DR techniques have additional parameters that affect the results, which make the task of choosing a technique more difficult. Existing methods compare DR techniques using some quality metrics, and some of them combine DR outputs by averaging projections. However, it does not yet provide enough mechanisms to create a new DR according to user requirements. In this paper, we present a way to analyze and compare different DR techniques. It is an interactive assessment method that allows a user to explore known DR techniques, identify the differences between them, and create a new DR technique that combines existing techniques to match user expectations.

The development of multimedia and network technologies strongly increase the interest on Internet broadcasting or streaming services, especially for soccer games. An example is the 2014 World Cup soccer tournament that registered record-breaking audiences worldwide, providing attractive alternatives to traditional TV viewing. The confluence of video streaming and computational resources opens up many possibilities for applications such as the online detection of interesting events, strategy analysis, or statistics comparisons. While much research targets algorithms to detect match statistics, strategy, retrieval, and indexing, the problem of presenting such information to users is much less studied. This paper proposes a simple but effective visual metaphor to help users browse and get insight into sports matches, with a focus on soccer games. We extract video segments, based on audio and metadata, identifying the main events of a game. Next, we use such events to define a visual representation that preserves the time-order of the video sequence, highlighting the most important events. Our visual representation enables the quick finding of the main events, allowing users to improve navigation when exploring a match, and also provides a way to evaluate the quality of a game or entire tournaments. We demonstrate our approach by applying it to several matches of 2014 World Cup, analyzing its knockout stage and comparing the final match in six different languages.

The discovery of patterns in large data collections is a difficult task. Visualization and machine learning techniques have emerged as a way to facilitate data analysis, providing tools to uncover relevant patterns from the data. This paper presents Concentric Rad Viz, a general purpose class visualization system that takes into account multi-class, multi-label and multi-task classifiers. Concentric Rad Viz uses a force attenuation scheme, which minimizes cluttering and ambiguity in the visual layout. In~addition, the user can add concentric circles to the layout in order to represent classification tasks. Our validation results and the application of Concentric Rad Viz for two real collections suggest that this tool can reveal important data patterns and relations. In our application, the user can interact with the visualization by selecting regions of interest according to specific criteria and changing projection parameters.

Users interact a lot with their personal music collections, typically using standard text-based interfaces that offer constrained functionalities based on assigned metadata or tags. Alternative visual interfaces have been developed, both to display graphical views of music collections that attempt to reflect some chosen property or organization, or to display abstract visual representations of specific songs. Yet, there are many dimensions involved in the perception and handling of music and mapping musical information into computer tractable models is a challenging problem. With a wide variety of possible approaches, the search for novel strategies to visually represent songs and/or collections persists, targeted either at the general public or at musically trained individuals. In this paper we describe a visual interface to browse music collections that relies on a graphical metaphor designed to convey the underlying musical structure of a song. An iconic representation of individual songs is coupled with a spatial placement of songs that reflects their structural similarity. The song icon is derived from features extracted from MIDI files, rather than from audio signals. The very nature of MIDI descriptions enables the identification of simple, yet meaningful, musical structures, allowing us to extract features that support both creating the icon and comparing songs. A similarity-based spatial placement is created projecting the feature vectors with the Least Square Projection multidimensional projection, employing the Dynamic Time Warping distance function to evaluate feature similarity. We describe the process of generating such visual representations and illustrate potentially interesting usage scenarios.

Most image colorization techniques assign colors to grayscale images by embedding image pixels into a high dimensional feature space and applying a color pattern to each cluster of high-dimensional data. A main drawback of such an approach is that, depending on texture patterns and image complexity, clusters of similar pixels can hardly be defined automatically, rendering existing methods prone to fail. In this work we present a novel approach to colorize grayscale images that allows for user intervention. Our methodology makes use of multidimensional projection to map high-dimensional data to a visual space. User can manipulate projected data in the visual space so as to further improve clusters and thus the colorization result. Different from other methods, our interactive tool is ease of use while still being flexible enough to enable local color modification. We show the effectiveness of our approach through a set of examples and comparisons against existing colorization methods.

Many image-related applications rely on the fact that the dataset under investigation is correctly represented by features. However, defining a set of features that properly represents a dataset is still a challenging and, in most cases, an exhausting task. Most of the available techniques, especially when a large number of features is considered, are based on purely quantitative statistical measures or approaches based on artificial intelligence, and normally are “black-boxes” to the user. The approach proposed here seeks to open this “black-box” by means of visual representations, enabling users to get insight about the meaning and representativeness of the features computed from different feature extraction algorithms and sets of parameters. The results show that, as the combination of sets of features and changes in parameters improves the quality of the visual representation, the accuracy of the classification for the computed features also improves. The results strongly suggest that our approach can be successfully employed as a guidance to defining and understanding a set of features that properly represents an image dataset.

Document interpretation is a crucial task in many visual analytics applications, made harder by the widespread availability of freely available textual files. In this paper we propose an approach based on topic detection coupled with multiple coordinated views to assist analysis of time varying document collections. Given multiple document maps built from a set of text files, we define a strategy to support users locating the evolution of topics addressed by the documents, along various time steps. The approach is supported by a new algorithm for topic extraction from texts, also introduced. Finally, we show several examples illustrating how the proposed strategy may be applied in the analysis of document collections.

Multidimensional visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces a methodology and a software tool called PEx-Image - Projection Explorer for Image for analysis and exploration of image collections employing visualizations. The visual mappings proposed here are similarity-based multidimensional projections and point placements, which layout the data on a plane for visual exploration. The proposed approach supports various image analysis tasks such as feature selection and classification, improving data exploration capabilities. We also illustrate how it can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.

Multiple views have been put forward as an alternative to assist exploration of evolving phenomena or associations between distinct data sets or distinct presentations of a single data set. Coordinating between views is a challenge that must be met to improve visualization support for exploratory tasks. This is particularly true for high-dimensional data, such as document collections. We introduce a coordination framework for multiple views of document collections created using projections and point placement visualizations. Coordination can occur between different views of a single data set or between views of multiple data sets. Multiple coordinations are also admitted. Three new types of coordination are presented to illustrate the framework; these have been implemented in a multipurpose multi-dimensional visualization system called PEx (Projection Explorer).

The efficacy of search engines has expanded the uses for the information available on the Web. An increasing number of applications make use of the WWW as a primary source of information. The usefulness of such applications is, however, impaired by the current styles of display of the Web search results. This paper presents a system that adapts two techniques to map and explore Web results visually in order to find relevant patterns and relationships amongst the resulting documents. The first technique creates a visual map of the search results using a content-based multidimensional projection. The second techniques is capable of identifying, labeling and displaying topics within sub-groups of documents on the map. The system (The Projection explorer for the WWW, or PEx-Web) implements these techniques and various additional tools as means to make better use of Web search results for exploratory applications.

Time series analysis poses many challenges to professionals in a wide range of domains. Several visualization solutions have been proposed for exploratory tasks on time series collections. For large data sets, however, current techniques fail to provide a global view that supports a good association between groups of similar time series. We employ fast multidimensional projection techniques to create concise visual representations of a collection of time series. The whole collection can be viewed in a two-dimensional graph-based representation that provides a starting point for further exploration and detailed analysis. The projections employ distance metrics to compare the series and generate a layout that attempts to group those with similar behavior. We illustrate the approach on a real data set containing streamflows describing the behavior of hydroelectric power plants in Brazil.

The task of building effective representations to visualize and explore collections with moderate to large number of documents is hard. It depends on the evaluation of some distance measure among texts and also on the representation of such relationships in bi- dimensional spaces. In this paper we introduce an alternative approach for building visual maps of documents based on their content similarity, through reconstruction of phylogenetic trees. The tree is capable of representing relationships that allows the user to quickly recover information detected by the similarity metric. For a variety of text collections of different natures we show that we can achieve improved exploration capability and more clear visualization of relationships amongst documents.

Recent improvements in mass spectrometry (MS) technology led to an explosive amount of MS data collected and shared. A typical liquid chromatography/mass spectrometry (LC/MS) "image" from the instrument used in this study consists of 4 GB of data. To reduce the bit rate required to code the MS data below that of the authors' previous (lossless) algorithm, we introduce a technique for near-lossless compression. It guarantees that each decompressed sample differs from its original value by no more than a user-specified quantity defined as the target maximum absolute distortion (MAD). We evaluate the proposed method by introducing feature-based metrics applied to the decompressed MS data and show that the MAD-based compression outperforms a traditional coding algorithm aimed at minimizing the mean squared error.

This paper presents a tool, called text map explorer, which can be used to create and explore document maps (visual representations of document collections). This tool is capable of grouping (and separating) documents by their contents, revealing to the user relationships amongst them. This paper also presents a novel multi-dimensional projection technique for text that reduces the quadratic time complexity of our previous approach to O(N3/2), keeping the same quality of maps. The technique creates a surface that reveals intrinsic patterns and supports various kinds of exploration of a text collection

This paper introduces Least Square Projection (LSP), a fast technique for projection of multi-dimensional data onto lower dimensions developed and tested successfully in the context of creation of text maps based on their content. Current solutions are either based on computationally expensive dimension reduction with no proper guarantee of the outcome or on faster techniques that need some sort of post-processing for recovering information lost during the process. LSP is based on least square approximation, a technique originally employed for surface modeling and reconstruction. Least square approximations are capable of computing the coordinates of a set of projected points based on a reduced number of control points with defined geometry. We extend the concept for general data sets. In order to perform the projection, a small number of distance calculations is necessary and no repositioning of the final points is required to obtain a satisfactory precision of the final solution. Textual information is a typically difficult data type to handle, due to its intrinsic dimensionality. We employ document corpora as a benchmark to demonstrate the capabilities of the LSP to group and separate documents by their content with high precision.

Summary form only given. The unrelenting growth of liquid chromatography-mass spectrometry (LC-MS) based proteomic data to gigabytes per sample and terabytes per experiment motivates this investigation into compression methods suited to MS signal sources. Compression is needed to facilitate storage, searching, archiving, retrieval, and communication of proteomic MS data. We demonstrate compression techniques that reduce the average file size by a factor of 25 without any loss of accuracy. We have designed two main methods to code the MS data. The first method predicts the mass-to-charge ratio based on the intensity values and encodes the residual with bzip2. The second algorithm maps the original intensity values onto a universal grid and either directly encodes them with bzip2 or applies an arithmetic coder to the results of run-length coding. The latter method achieves the highest compression ratios.