Dimensionality reduction for documents with nearest neighbor queries

Ingram, Stephen; Munzner, Tamara

doi:10.1016/j.neucom.2014.07.073

Cited by 39 publications

(29 citation statements)

References 24 publications

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“…(2) shows that the CMDS solution of the set is equivalent to the image matrix of the original coordinate matrix under the isometric transformation ,…”

Section: U Xhmentioning

confidence: 99%

“…The second step of the algorithm is to find the solutions of the submatrices (subsets). Use (2) to write the CMDS solution of (5) …”

Section: U Xhmentioning

confidence: 99%

“…CMDS is a common method for dimensionality reduction and data visualization [2]- [5] and can also be applied in the fields of robotics and biomimetics [6]- [8]. CMDS routine is contained in MATLAB, SAS, SPSS, STATISTICA, S-Plus, etc.…”

Section: Introductionmentioning

confidence: 99%

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A fast multidimensional scaling algorithm

Cai

2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Classical multidimensional scaling (CMDS) is a common method for dimensionality reduction and data visualization. Aimed at the problem of slow speed of CMDS, a divide-and-conquer based MDS (dcMDS) algorithm is put forward in this paper. In this algorithm, the distance matrix between samples is divided along its main diagonal into several submatrices ， which are solved respectively. By isometric transformation, the solutions of the submatrices can be integrated to form the solution of the whole matrix. The solution of dcMDS is the same as that of CMDS. Moreover, when the intrinsic dimension of the samples is much smaller than the number of samples, the speed of dcMDS is significantly improved than CMDS. In this paper, a detailed theoretical analysis of dcMDS is presented, and its efficiency is verified by experiments.

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“…(2) shows that the CMDS solution of the set is equivalent to the image matrix of the original coordinate matrix under the isometric transformation ,…”

Section: U Xhmentioning

confidence: 99%

“…The second step of the algorithm is to find the solutions of the submatrices (subsets). Use (2) to write the CMDS solution of (5) …”

Section: U Xhmentioning

confidence: 99%

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A fast multidimensional scaling algorithm

Cai

2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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“…As shown in [3][4][5], the results of MDS mapping will improve dramatically when the cost function V (Δ, d) is represented by the Kullback-Leibler (K-L) metrics. Instead of minimizing the error between Δ and d, the K-L divergence computes the distance between probability densities of the nearest-neighbors occurrence in Ω and X spaces, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…As shown in [4,5], visualization of large data consisting of 10 5 + of objects requires approximated versions of MDS. They can be developed by limiting the number of computed distances, e.g., via random sampling [8,9], landmark particles [8], core points selection, hierarchical clustering and k-NN interpolation [10,11], or by using more sophisticated thinning or approximation procedures such as: deep belief networks (DBN) [12], Barnes-Hut-SNE [4], Q-SNE [5] or LoCH [10]. There are also many parallel realization of approximated versions of MDS including such the solvers as SMA-COF [13], GLIMMER [11] and SUBSET [8].…”

Section: Introductionmentioning

confidence: 99%

Visual Exploration of Data with Multithread MIC Computer Architectures

Pawliczek

Dzwinel

Yuen

2015

Artificial Intelligence and Soft Computing

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Abstract. Knowledge mining from immense datasets requires fast, reliable and affordable tools for their visual and interactive exploration. Multidimensional scaling (MDS) is a good candidate for embedding of high-dimensional data into visually perceived 2-D and 3-D spaces. We focus here on the way to increase the computational performance of MDS in the context of interactive, hierarchical, visualization of big data. To this end we propose a parallel implementation of MDS on the modern Intel Many Integrated Core Architecture (MIC). We compare the timings obtained for MIC architecture to GPU and standard multi-core CPU implementations of MDS. We conclude that despite 30-40% lower computational performance comparing to GPU/CUDA tuned MDS codes, the MIC solution is still competitive due to dramatically shorter code production and tuning time. The integration of MIC with CPU will make this architecture very competitive with more volatile on technological changes GPU solutions.

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Low‐Dimensional Embeddings for Interaction Design

Rusu

Schött

Williamson

et al. 2021

Advanced Intelligent Systems

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From the coffee mug we put to our mouths, to our fingers finding their way to the correct letters on our keyboard, hand movements are ever-present. Using data gloves, optical tracking, or accelerometers, a lot of information about these habitual interactions can be captured, showing that, as well as dominating everyday life, they can also be quite complex. [4] Designing for more interactive objects, with hand gestures acting as one of the main controls, is an area of increasing research and commercial interest. While the degrees of freedom (DOF) the human hand possesses allow for "flexibility to perform skilled finger movements" [5] in real life, they pose technological challenges for digital interfaces, as most typical interaction design processes are not yet well adapted to high-dimensional input. [6] The goal when prototyping such controls is to find gestures that are easy for the user to recall and perform reliably, and easy for the computer to distinguish from one another. However, gestures possess a high degree of complexity and human movement possesses a variability [7] that needs to be known and incorporated into the design process. Confronting designers with the raw high-dimensional data as input and asking them to design robust, usable interactive systems has proven to be challenging. This has limited the impact of novel sensing systems that capturing more of the complexity of real-life interactions. Such systems' datasets possess higher dimensionalities, which makes them more challenging for designers to understand and utilize. Lu et al., for example, are developing a system that can capture the full 25 dimensions hand movement possesses with the help of multiple inertial sensors. [8] Further, large amounts of high-dimensional data can be generated from the deployment of multiple sensors across physical objects, as is the case for the WebBike Project, where the researchers equipped e-bikes with sensors. [9] The highly complex datasets created by those novel sensing systems require a layer of simplification to become comprehensible for researchers and designers and to proactively apply design processes appropriately. We explore the role of lowdimensional embeddings in conceptualizing and visualizing high-dimensional movement data by answering the following research question.Can Autoencoder-Based Dimensionality Reduction Simplify the Data-Driven Design Process?Our findings suggest that the low-dimensional representation of complex movement data is more convenient to work with than high-dimensional data, which builds a basis for analyzing and visualizing hand movements and interactions among team members during the design process. Further, we define

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Dimensionality reduction for documents with nearest neighbor queries

Cited by 39 publications

References 24 publications

A fast multidimensional scaling algorithm

A fast multidimensional scaling algorithm

Visual Exploration of Data with Multithread MIC Computer Architectures

Low‐Dimensional Embeddings for Interaction Design

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