In this paper we present the PK-tree which is an index structure for high dimensional point data. The proposed indexing structure can be viewed as combining aspects of the PR-quad or K-D tree but where unnecessary nodes are eliminated. The unnecessary nodes are typically the result of skew in the point distribution and we show that by eliminating these nodes the performance of the resulting ind...

Background: Biomedical curators are often required to semantically index large numbers of biomedical articles, using hierarchically related labels (e.g., MeSH headings). Large scale hierarchical classification, a branch of machine learning, can facilitate this procedure, but the resulting automatic classifiers are often inefficient because of the very large dimensionality of the dominant bag-of...

Data structures for similarity search are commonly evaluated on data in vector spaces, but distance-based data structures are also applicable to non-vector spaces with no natural concept of dimensionality. The intrinsic dimensionality statistic of Chávez and Navarro provides a way to compare the performance of similarity indexing and search algorithms across different spaces, and predict the pe...

As an effective way in finding the underlying parameters of a high-dimension space, manifold learning is popular in nonlinear dimensionality reduction which makes high-dimensional data easily to be observed and analyzed. In this paper, Isomap, one of the most famous manifold learning algorithms, is applied to process closing prices of stocks of CSI 300 index from September 2009 to October 2011....

Whereas optical character recognition (OCR) systems learn to classify single characters; people learn to classify long character strings in parallel, within a single fixation . This difference is surprising because high dimensionality is associated with poor classification learning. This paper suggests that the human reading system avoids these problems because the number of to-be-classified im...

Dimensionality reduction and the automatic learning of key features from electroencephalographic (EEG) signals have always been challenging tasks. Variational autoencoders (VAEs) used for EEG data generation augmentation, denoising, feature extraction. However, investigations optimal shape their latent space neglected. This research tried to understand minimal size convolutional VAEs, trained w...

The main aim of this paper is to perform the analysis of Principal Component Analysis (PCA) as a Dimensionality Reduction technique and Sparse Representation Classifier (SRC) as a Post Classifier for the Classification of Epilepsy Risk levels from Electroencephalography signals. The data acquisition of the EEG signals is performed initially. Then PCA is applied here as a dimensionality reductio...

In this paper, we study spectrum sensing based on dimensionality reduction and random forest (RF) in low signal-to-noise ratio environments. Classifications of three digital modulation types, including BPSK, OFDM and 2FSK, are investigated. From the received radio signal, a set of cyclic spectrum features are first calculated, and the principal component analysis (PCA) is applied to extract the...

The main aim of this paper is to perform the analysis of Singular Value Decomposition (SVD) as a Dimensionality Reduction technique and Sparse Representation Classifier (SRC) as a Post Classifier for the Classification of Epilepsy Risk levels from Electroencephalography signals. The data acquisition of the EEG signals is performed initially. Then SVD is applied here as a dimensionality reductio...