The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform

Shuoyu Wang - One of the best experts on this subject based on the ideXlab platform.

  • BICT - SVD-based feature extraction from time-series motion data and its application to gesture recognition
    Proceedings of the 8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2015
    Co-Authors: Isao Hayashi, Yinlai Jiang, Shuoyu Wang
    Abstract:

    Singular value decomposition is used to extract features from time-series motion data. A matrix consisting of the time-series data is decomposed into Left Singular Vectors which represent the patterns of the motion and Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Gesture recognition using the extracted features suggest the effectiveness of the method.

  • Knowledge Acquisition Method Based on Singular Value Decomposition for Human Motion Analysis
    IEEE Transactions on Knowledge and Data Engineering, 2014
    Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu Wang
    Abstract:

    The knowledge remembered by the human body and reflected by the dexterity of body motion is called embodied knowledge. In this paper, we propose a new method using Singular value decomposition for extracting embodied knowledge from the time-series data of the motion. We compose a matrix from the time-series data and use the Left Singular Vectors of the matrix as the patterns of the motion and the Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Two experiments were conducted to validate the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with indexes of similarity and estimation that use Left Singular Vectors. The proposed method obtained a higher correct categorization ratio than principal component analysis (PCA) and correlation efficiency (CE). The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability. The first Singular values derived from the walking acceleration were suggested to be a reliable criterion to evaluate walking disability. Finally we discuss the characteristic and significance of the embodied knowledge extraction using the Singular value decomposition proposed in this paper.

  • FUZZ-IEEE - Embodied knowledge extraction from human motion using Singular value decomposition
    2012 IEEE International Conference on Fuzzy Systems, 2012
    Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu Wang
    Abstract:

    Embodied knowledge is the knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using Singular value decomposition for extracting embodied knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the Left Singular Vectors of the matrix as the patterns of the motion and the Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using Left Singular Vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the Singular values. Finally we discuss the characteristic and significance of the embodied knowledge extraction using Singular value decomposition proposed in this paper.

Gordon S. Okimoto - One of the best experts on this subject based on the ideXlab platform.

  • CDC - Integrated analysis of multiple high-dimensional data sets by joint rank-1 matrix approximations
    2015 54th IEEE Conference on Decision and Control (CDC), 2015
    Co-Authors: Ashkan Zeinalzadeh, Tom Wenska, Gordon S. Okimoto
    Abstract:

    In this work, we developed an algorithm for the integrated analysis of multiple high-dimensional data matrices based on sparse rank-one matrix approximations. The algorithm approximates multiple data matrices with rank one outer products composed of sparse Left Singular-Vectors that are unique to each matrix and a right Singular-Vector that is shared by all of the data matrices. The right-Singular Vector represents a signal we wish to detect in the row-space of each matrix. The non-zero components of the resulting Left-Singular Vectors identify rows of each matrix that in aggregate provide a sparse linear representation of the shared right-Singular Vector. This sparse representation facilitates downstream interpretation and validation of the resulting model based on the rows selected from each matrix. False discovery rate is used to select an appropriate l1 penalty parameter that imposes sparsity on the Left Singular-Vector but not the common right Singular-Vector of the joint approximation. Since a given multi-modal data set (MMDS) may contain multiple signals of interest the algorithm is iteratively applied to the residualized version of original data to sequentially capture and model each distinct signal in terms of rows from the different matrices. We show that the algorithm outperforms standard Singular value decomposition over a wide range of simulation scenarios in terms of detection accuracy. Analysis of real data for ovarian and liver cancer resulted in compact gene expression signatures that were predictive of clinical outcomes and highly enriched for cancer related biology.

Yinlai Jiang - One of the best experts on this subject based on the ideXlab platform.

  • BICT - SVD-based feature extraction from time-series motion data and its application to gesture recognition
    Proceedings of the 8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2015
    Co-Authors: Isao Hayashi, Yinlai Jiang, Shuoyu Wang
    Abstract:

    Singular value decomposition is used to extract features from time-series motion data. A matrix consisting of the time-series data is decomposed into Left Singular Vectors which represent the patterns of the motion and Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Gesture recognition using the extracted features suggest the effectiveness of the method.

  • Knowledge Acquisition Method Based on Singular Value Decomposition for Human Motion Analysis
    IEEE Transactions on Knowledge and Data Engineering, 2014
    Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu Wang
    Abstract:

    The knowledge remembered by the human body and reflected by the dexterity of body motion is called embodied knowledge. In this paper, we propose a new method using Singular value decomposition for extracting embodied knowledge from the time-series data of the motion. We compose a matrix from the time-series data and use the Left Singular Vectors of the matrix as the patterns of the motion and the Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Two experiments were conducted to validate the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with indexes of similarity and estimation that use Left Singular Vectors. The proposed method obtained a higher correct categorization ratio than principal component analysis (PCA) and correlation efficiency (CE). The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability. The first Singular values derived from the walking acceleration were suggested to be a reliable criterion to evaluate walking disability. Finally we discuss the characteristic and significance of the embodied knowledge extraction using the Singular value decomposition proposed in this paper.

  • FUZZ-IEEE - Embodied knowledge extraction from human motion using Singular value decomposition
    2012 IEEE International Conference on Fuzzy Systems, 2012
    Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu Wang
    Abstract:

    Embodied knowledge is the knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using Singular value decomposition for extracting embodied knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the Left Singular Vectors of the matrix as the patterns of the motion and the Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using Left Singular Vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the Singular values. Finally we discuss the characteristic and significance of the embodied knowledge extraction using Singular value decomposition proposed in this paper.

Ekawit Nantajeewarawat - One of the best experts on this subject based on the ideXlab platform.

  • the Vector space models for finding co occurrence names as aliases in thai sports news
    Asian Conference on Intelligent Information and Database Systems, 2010
    Co-Authors: Thawatchai Suwanapong, Thanaruk Theeramunkong, Ekawit Nantajeewarawat
    Abstract:

    Discovering aliases in Thai sports news is a challenging task. This paper presents an approach to identifying aliases by analyzing cooccurrence relationships between named entities. Semantically similar names are computed using two Vector methods - Latent Semantic Analysis (LSA) and correlation matrix (COM). The LSA method decomposes a name-by-document matrix (NDM) into Singular-value and Singular-Vector matrices. The truncated Left Singular Vector matrix is used for identifying name similarity. The COM method constructs a name-byname matrix (NNM) from the NDM and then directly measures similarity among name Vectors using simple calculations. Both methods are weighted by the same weighting schemes. Obtained similarity relations among names are filtered out based on name types. Our preliminary experimental results show that the COM method performs better than the LSA method.

  • ACIIDS (1) - The Vector space models for finding co-occurrence names as aliases in Thai sports news
    Intelligent Information and Database Systems, 2010
    Co-Authors: Thawatchai Suwanapong, Thanaruk Theeramunkong, Ekawit Nantajeewarawat
    Abstract:

    Discovering aliases in Thai sports news is a challenging task. This paper presents an approach to identifying aliases by analyzing cooccurrence relationships between named entities. Semantically similar names are computed using two Vector methods - Latent Semantic Analysis (LSA) and correlation matrix (COM). The LSA method decomposes a name-by-document matrix (NDM) into Singular-value and Singular-Vector matrices. The truncated Left Singular Vector matrix is used for identifying name similarity. The COM method constructs a name-byname matrix (NNM) from the NDM and then directly measures similarity among name Vectors using simple calculations. Both methods are weighted by the same weighting schemes. Obtained similarity relations among names are filtered out based on name types. Our preliminary experimental results show that the COM method performs better than the LSA method.

Isao Hayashi - One of the best experts on this subject based on the ideXlab platform.

  • BICT - SVD-based feature extraction from time-series motion data and its application to gesture recognition
    Proceedings of the 8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2015
    Co-Authors: Isao Hayashi, Yinlai Jiang, Shuoyu Wang
    Abstract:

    Singular value decomposition is used to extract features from time-series motion data. A matrix consisting of the time-series data is decomposed into Left Singular Vectors which represent the patterns of the motion and Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Gesture recognition using the extracted features suggest the effectiveness of the method.

  • Knowledge Acquisition Method Based on Singular Value Decomposition for Human Motion Analysis
    IEEE Transactions on Knowledge and Data Engineering, 2014
    Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu Wang
    Abstract:

    The knowledge remembered by the human body and reflected by the dexterity of body motion is called embodied knowledge. In this paper, we propose a new method using Singular value decomposition for extracting embodied knowledge from the time-series data of the motion. We compose a matrix from the time-series data and use the Left Singular Vectors of the matrix as the patterns of the motion and the Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Two experiments were conducted to validate the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with indexes of similarity and estimation that use Left Singular Vectors. The proposed method obtained a higher correct categorization ratio than principal component analysis (PCA) and correlation efficiency (CE). The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability. The first Singular values derived from the walking acceleration were suggested to be a reliable criterion to evaluate walking disability. Finally we discuss the characteristic and significance of the embodied knowledge extraction using the Singular value decomposition proposed in this paper.

  • FUZZ-IEEE - Embodied knowledge extraction from human motion using Singular value decomposition
    2012 IEEE International Conference on Fuzzy Systems, 2012
    Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu Wang
    Abstract:

    Embodied knowledge is the knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using Singular value decomposition for extracting embodied knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the Left Singular Vectors of the matrix as the patterns of the motion and the Singular values as a scalar, by which each corresponding Left Singular Vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using Left Singular Vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the Singular values. Finally we discuss the characteristic and significance of the embodied knowledge extraction using Singular value decomposition proposed in this paper.