Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dim...
Yanwei Pang, Lei Zhang, Zhengkai Liu, Nenghai Yu, ...
In this paper we present a fusion technique for Support Vector Machine (SVM) scores, obtained after a dimension reduction with Bilateralprojection-based Two-Dimensional Principal C...
Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are r...
Increasingly large text datasets and the high dimensionality associated with natural language create a great challenge in text mining. In this research, a systematic study is cond...
M. Mahdi Shafiei, Singer Wang, Roger Zhang, Evange...
There are several pieces of information that can be utilized in order to improve the efficiency of similarity searches on high-dimensional data. The most commonly used information...
Abstract. Joint diagonalization for ICA is often performed on the orthogonal group after a pre-whitening step. Here we assume that we only want to extract a few sources after pre-w...
Fabian J. Theis, Thomas P. Cason, Pierre-Antoine A...
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensional...