• Journal of the Korean Data and Information Science Society
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• 제5권1호
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• pp.1-10
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• 1994

Projection pursuit(PP) is a computer-intensive method which seeks out interesting linear projections of multivariate data onto a lower dimension space by machine. By working with lower dimensional projections, projection pursuit avoids the sparseness of high dimensional data. We show through simulation that two projection pursuit discriminant mothods proposed by Chen(1989) and Huber(1985) do not improve very much the error rate than the existing methods and compare several classification procedures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2991-3007
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• 2022

Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It's able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don't always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4429-4447
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• 2018

Local binary descriptors are well-suited for many real-time and/or large-scale computer vision applications, while their low computational complexity is usually accompanied by the limitation of performance. In this paper, we propose a new optimization framework, RLDB (Robust-LDB), to improve a typical region-based binary descriptor LDB (local difference binary) and maintain its computational simplicity. RLDB extends the multi-feature strategy of LDB and applies a more complete region-comparing configuration. A cascade bit selection method is utilized to select the more representative patterns from massive comparison pairs and an online learning strategy further optimizes descriptor for each specific patch separately. They both incorporate LDP (linear discriminant projections) principle to jointly guarantee the robustness and distinctiveness of the features from various scales. Experimental results demonstrate that this integrated learning framework significantly enhances LDB. The improved descriptor achieves a performance comparable to floating-point descriptors on many benchmarks and retains a high computing speed similar to most binary descriptors, which better satisfies the demands of applications.

• 응용통계연구
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• 제5권1호
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• pp.103-111
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• 1992

다변량 통계분석기법중 하나로 제기된 투사지향방법은 다변량자료를 관심있는 일차원 또는 이차원의 자료로의 선형투사를 찾아 나가는 방법이다. 이 방법은 다변량 자료가 갖는 차원의 문제를 해결해 줄 수 있는 유용한 기법으로 제시되었다. 본 연구에서는 투사지향방법을 이용하여 추정한 다변량 확률밀도함수를 사용한 새로운 비모수적인 판별분석방법을 제시하고, 이를 기존의 모수적 판별분석방법중 실제적으로 많이 사용되는 선형판별함수방법, 그리고 기존의 비모수적 판별분석방법중 계산상의 편리성이 많은 K-최인접방법과 컴퓨터 시뮬레이션을 통하여 비교분석하였다.