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Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label...
This research is concerned with the table based KNN as the approach to the keyword extraction task. The keyword extraction task is viewed as an instance of word classification, and it is discovered that encoding words into tables improved the word categorization performance. In this research, words are encoded into tables and the correspondingly modified version of KNN is applied to the keyword extraction...
In this research, we propose the table based KNN as the approach to the text categorization. In previous works, we discovered that encoding texts into tables improved the performance in the text categorization, so in this research, become to consider the possibility of encoding words into tables as well as texts. In this research, we encode words into tables where entries are texts and their weights,...
We concern this research with the table based KNN as the approach to the index optimization task. It may be interpreted into an instance of word classification, and the encoding scheme where words are encoded into tables improved the task word classification. In this research, words are encoded into tables and apply the table based KNN to the index optimization task. From this research, we expect...
This research proposes the table based AHC algorithm as the approach to the word clustering task. The results from encoding texts into tables were successful in the previous works on the text categorization and the text clustering, and if oppositely to the case of the text encoding, texts are assumed to be elements of each word, it becomes to be possible to encode words into tables. In this research,...
The analysis of human brain connectivity networks has become an increasingly prevalent task in neuroimaging. A few recent studies have shown the possibility of decoding brain states based on brain graph classification. Graph kernels have emerged as a powerful tool for graph comparison that allows the direct use of machine learning classifiers on brain graph collections. They allow classifying graphs...
Nowadays the bag-of-visual-words is a very popular approach to perform the task of Visual Object Classification (VOC). Two key phases of VOC are the vocabulary building step, i.e. the construction of a ‘visual dictionary’ including common codewords in the image corpus, and the assignment step, i.e. the encoding of the images by means of these codewords. Hard assignment of image descriptors to visual...
This paper introduces a novel coding scheme based on the diffusion map framework. The idea is to run a t-step random walk on the data graph to capture the similarity of a data point to the codebook atoms. By doing this we exploit local similarities extracted from the data structure to obtain a global similarity which takes into account the non-linear structure of the data. Unlike the locality-based...
Unsupervised clustering of large data sets is a complicated NP-hard task. Due to its complexity, various metaheuristic machine learning algorithms have been used to automate or aid the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success. However, also evolutionary clustering suffers from the high computational demands when it comes...
Semi-supervised learning uses both labeled and unlabeled data for machine learning tasks. It's especially useful in the scenarios where labeled data is very scarce or expensive to obtain. In this work, we present kernel LLC, the kernel locality-constrained linear coding within a data-dependent kernel space, for data representation. The data-dependent kernel captures the underlying data geometry on...
A regularized method to incorporate prior knowledge into spectral clustering in the form of pairwise constraints is proposed. This method is based on a weighted kernel principal component analysis (PCA) interpretation of spectral clustering with primal-dual least squares support vector machines (LS-SVM) formulations. The weighted kernel PCA framework allows incorporating pairwise constraints into...
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