The Experts below are selected from a list of 3540 Experts worldwide ranked by ideXlab platform
Yang Jingyu - One of the best experts on this subject based on the ideXlab platform.
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Affine Subspace nearest points classification algorithm
2008Co-Authors: Yang JingyuAbstract:A novel pattern classification algorithm called Affine Subspace Nearest Points(ASNP)algorithm is presented in this paper.Inspired by the geometrical explanation of Support Vector Machine(SVM)and the nearest point method,in which the optimal separating plane bisects the closest points within two convex hulls,the ASNP algorithm expands the searching areas of the closest points from the convex hulls to their corresponding class Affine Subspaces.The Affine Subspaces are taken as the rough estimations of the class sample distributions,and their closest points are found.Then,the hyperplane to separate the Affine Subspaces with the maximal margins is constructed,which is the perpendicular bisector of the line segment joining the two closest points.The test experiments compared with the Nearest Neighbor(1-NN)classifier and SVM on the ORL face recognition database showed good performance of this algorithm.
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kernel Affine Subspace nearest points classification algorithm
2008Co-Authors: Yang JingyuAbstract:A novel pattern recognition algorithm called Kernel Affine Subspace Nearest Points(KASNP) classification is presented.Inspired by the geometrical explanation of Support Vector Machine(SVM) that the optimal separating plane bisects the closest points within two class convex hulls,KASNP algorithm expands the searching areas of the closest points from the convex hulls to their corresponding class Affine Subspaces in kernel space.The Affine Subspaces are taken as the rough estimations of the class feature sample distributions,and their closest points are found.The hyperplane to separate the Affine Subspaces with the maximal margins is constructed,which is the perpendicular bisector of the line segment joining the two closest points.The test experiments compared with the Nearest Neighbor(1-NN) classifier and SVM on the ORL face recognition database show good performance of this algorithm.
Kohichi Sakaniwa - One of the best experts on this subject based on the ideXlab platform.
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spatially coupled binary mackay neal codes for channels with non binary inputs and Affine Subspace outputs
2012Co-Authors: Kenta Kasai, Takayuki Nozaki, Kohichi SakaniwaAbstract:We study LDPC codes for the channel with 2m-ary input x ∊ Fm and output y = x + z ∊ F 2 m. The receiver knows a Subspace V ⊂ F 2 m from which z = y − x is uniformly chosen. Or equivalently, the receiver receives an Affine Subspace y-V where x lies. We consider a joint iterative decoder involving the channel detector and the LDPC decoder. The decoding system considered in this paper can be viewed as a simplified model of the joint iterative decoder over non-binary modulated signal inputs e.g., 2m-QAM. We evaluate the performance of binary spatially-coupled MacKay-Neal codes by density evolution. The iterative decoding threshold is seriously degraded by increasing m. EXIT-like function curve calculations reveal that this degradation is caused by wiggles and can be mitigated by increasing the randomized window size. The resultant iterative decoding threshold values are very close to the Shannon limit.
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spatially coupled binary mackay neal codes for channels with non binary inputs and Affine Subspace outputs
2012Co-Authors: Kenta Kasai, Takayuki Nozaki, Kohichi SakaniwaAbstract:We study LDPC codes for the channel with $2^m$-ary input $\underline{x}\in \mathbb{F}_2^m$ and output $\underline{y}=\underline{x}+\underline{z}\in \mathbb{F}_2^m$. The receiver knows a Subspace $V\subset \mathbb{F}_2^m$ from which $\underline{z}=\underline{y}-\underline{x}$ is uniformly chosen. Or equivalently, the receiver receives an Affine Subspace $\underline{y}-V$ where $\underline{x}$ lies. We consider a joint iterative decoder involving the channel detector and the LDPC decoder. The decoding system considered in this paper can be viewed as a simplified model of the joint iterative decoder over non-binary modulated signal inputs e.g., $2^m$-QAM. We evaluate the performance of binary spatially-coupled MacKay-Neal codes by density evolution. The iterative decoding threshold is seriously degraded by increasing $m$. EXIT-like function curve calculations reveal that this degradation is caused by wiggles and can be mitigated by increasing the randomized window size. The resultant iterative decoding threshold values are very close to the Shannon limit.
Jacek Marchwicki - One of the best experts on this subject based on the ideXlab platform.
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levy steinitz theorem and achievement sets of conditionally convergent series on the real plane
2018Co-Authors: Szymon Glab, Jacek MarchwickiAbstract:Abstract Levy–Steinitz theorem characterizes the sum range of conditionally convergent series, that is a set of all its convergent rearrangements; in finitely dimensional spaces – it is an Affine Subspace. An achievement of a series is a set of all its subsums. We study the properties of achievement sets of series whose sum range is the whole plane. It turns out that it varies on the number of Levy vectors of a series.
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levy steinitz theorem and achievement sets of conditionally convergent series on the real plane
2017Co-Authors: Szymon Glab, Jacek MarchwickiAbstract:Levy-Steinitz theorem characterize sum range of conditionally convergent series, that is a set of all its convergent rearrangements; in finitely dimensional spaces -- it is an Affine Subspace. An achievement of a series is a set of all its subsums. We study the properties of achievement sets of series whose sum range is the whole plane. It turns out that it varies on the number of Levy vectors of a series.
Masahiro Yukawa - One of the best experts on this subject based on the ideXlab platform.
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adaptive learning in cartesian product of reproducing kernel hilbert spaces
2015Co-Authors: Masahiro YukawaAbstract:We propose a novel adaptive learning algorithm based on iterative orthogonal projections in the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The objective is to estimate or track nonlinear functions that are supposed to contain multiple components such as i) linear and nonlinear components and ii) high- and low- frequency components. In this case, the use of multiple RKHSs permits a compact representation of multicomponent functions. The proposed algorithm is where two different methods of the author meet: multikernel adaptive filtering and the algorithm of hyperplane projection along Affine Subspace (HYPASS). In a particular case, the “sum” space of the RKHSs is isomorphic, under a straightforward correspondence, to the product space, and hence the proposed algorithm can also be regarded as an iterative projection method in the sum space. The efficacy of the proposed algorithm is shown by numerical examples.
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an efficient kernel adaptive filtering algorithm using hyperplane projection along Affine Subspace
2012Co-Authors: Masahiro Yukawa, Ryuichiro IshiiAbstract:We propose a novel kernel adaptive filtering algorithm that selectively updates a few coefficients at each iteration by projecting the current filter onto the zero instantaneous-error hyperplane along a certain time-dependent Affine Subspace. Coherence is exploited for selecting the coefficients to be updated as well as for measuring the novelty of new data. The proposed algorithm is a natural extension of the normalized kernel least mean squares algorithm operating iterative hyperplane projections in a reproducing kernel Hilbert space. The proposed algorithm enjoys low computational complexity. Numerical examples indicate high potential of the proposed algorithm.
Kenta Kasai - One of the best experts on this subject based on the ideXlab platform.
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spatially coupled binary mackay neal codes for channels with non binary inputs and Affine Subspace outputs
2012Co-Authors: Kenta Kasai, Takayuki Nozaki, Kohichi SakaniwaAbstract:We study LDPC codes for the channel with 2m-ary input x ∊ Fm and output y = x + z ∊ F 2 m. The receiver knows a Subspace V ⊂ F 2 m from which z = y − x is uniformly chosen. Or equivalently, the receiver receives an Affine Subspace y-V where x lies. We consider a joint iterative decoder involving the channel detector and the LDPC decoder. The decoding system considered in this paper can be viewed as a simplified model of the joint iterative decoder over non-binary modulated signal inputs e.g., 2m-QAM. We evaluate the performance of binary spatially-coupled MacKay-Neal codes by density evolution. The iterative decoding threshold is seriously degraded by increasing m. EXIT-like function curve calculations reveal that this degradation is caused by wiggles and can be mitigated by increasing the randomized window size. The resultant iterative decoding threshold values are very close to the Shannon limit.
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spatially coupled binary mackay neal codes for channels with non binary inputs and Affine Subspace outputs
2012Co-Authors: Kenta Kasai, Takayuki Nozaki, Kohichi SakaniwaAbstract:We study LDPC codes for the channel with $2^m$-ary input $\underline{x}\in \mathbb{F}_2^m$ and output $\underline{y}=\underline{x}+\underline{z}\in \mathbb{F}_2^m$. The receiver knows a Subspace $V\subset \mathbb{F}_2^m$ from which $\underline{z}=\underline{y}-\underline{x}$ is uniformly chosen. Or equivalently, the receiver receives an Affine Subspace $\underline{y}-V$ where $\underline{x}$ lies. We consider a joint iterative decoder involving the channel detector and the LDPC decoder. The decoding system considered in this paper can be viewed as a simplified model of the joint iterative decoder over non-binary modulated signal inputs e.g., $2^m$-QAM. We evaluate the performance of binary spatially-coupled MacKay-Neal codes by density evolution. The iterative decoding threshold is seriously degraded by increasing $m$. EXIT-like function curve calculations reveal that this degradation is caused by wiggles and can be mitigated by increasing the randomized window size. The resultant iterative decoding threshold values are very close to the Shannon limit.