منابع مشابه
Bilinear Discriminant Component Analysis
Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data arrays. For three dimensional arrays, which may organize data in dimensions such as space, time, and trials, the opportunity arises to combine these two approaches. A new method, Bilinear Discriminant Component Analysis (BDCA), is derived and demonstrated in...
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In face recognition, feature extraction techniques attempts to search for appropriate representation of the data. However, when the feature dimension is larger than the samples size, it brings performance degradation. Hence, we propose a method called Normalization Discriminant Independent Component Analysis (NDICA). The input data will be regularized to obtain the most reliable features from t...
متن کاملIncremental Constrained Discriminant Component Analysis
Recently, a constrained Linear Discriminant Analysis (LDA) algorithm is introduced and gained popularity. However, this algorithm is not applicable in the environment with large amount of data points or when the data point arrive in a sequential manner. In this paper, we aim to propose an incremental version of this algorithm called Incremental Constrained Discriminant Component Analysis (ICDCA...
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We propose using a feature extraction scheme, Dis-criminant Component Analysis, for face recognition. This scheme decomposes a signal into orthogonal bases such that for each base there is an eigenvalue representing the discriminatory power of projection in that direction. The bases and eigenvalues are obtained by iteratively applying Fisher's Linear Discriminant Analysis (LDA). We illustrate t...
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ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2016
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2016.2539502