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Background Major depressive disorder (MDD) is highly heterogeneous in pathogenesis and manifestations. Further classification may help characterize its heterogeneity. We previously have shown differential metabolomic profiles of traditional Chinese medicine (TCM) diagnostic subtypes of MDD. We further determined brain connectomic associations with TCM subtypes of MDD. Methods In this naturalistic...
Background Major depressive disorder (MDD) is a highly heterogeneous disease. Further classification may characterize its heterogeneity. The purpose of this study was to examine whether metabolomic variables could differentiate traditional Chinese medicine (TCM) diagnostic subtypes of MDD. Methods Fifty medication-free patients who were experiencing a recurrent depressive episode were classified...
In the calculation of rank minimization, the non-negative sparse low-rank representation classification (NSLRRC) regularizes nuclear norm's each singular value equally, but this limits its flexibility and ability to solve many practical problems, where the singular values with clear physical meanings ought to be treated differently. In this paper, a weighted non-negative sparse low-rank representation...
This paper presents a novel method for robust face recognition, termed non-negative sparse low-rank representation classification (NSLRRC). NSLRRC seeks a sparse, low-rank and non-negative matrix over all training samples. Sparse constraint makes representation vector discriminative, while low-rank matrix will expose the global structures of data. Meanwhile, non-negative representation vectors guarantee...
Taking the features of data in low and high frequency texts and the frequencies which such features emerge in a single text into consideration, the paper sets up a vector space model for part of texts of field. Then the paper also establishes a classifying and clustering method with features of classification and clustering by designing and constructing the two-dimensional analytic indexes of similarities...
LSI can be regarded as a mapping of vector space model. Through carrying singular value decomposition computation on the word-text matrix in original text sets, the relationship among the latent connotation concepts in the documents sets can be calculated. Expressing all the concepts space by latent concept sets reduces the fuzziness among the concept expression and avoids the supposition that concept...
Feature selection method based on text study is a mainstream method currently, whose research key lies in finding out one suitable feature assessment method, which can reduce the numbers of the words to be processed as less as possible in the situation of not decreasing classification precision, to improve the speed and the efficiency of classification. A new feature assessment method entropy ratio...
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