scholarly journals A New Burrows Wheeler Transform Markov Distance

2020 ◽  
Vol 34 (04) ◽  
pp. 5444-5453
Author(s):  
Edward Raff ◽  
Charles Nicholas ◽  
Mark McLean
Keyword(s):  
Dna Sequence ◽  
Distance Measure ◽  
Feature Vector ◽  
Distance Metrics ◽  
Prior Work ◽  
Compression Algorithms ◽  
Fixed Length ◽  
Malware Classification ◽  
Classification Tasks ◽  
Burrows Wheeler Transform

Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. We describe issues with this approach that were not widely known, and introduce our new Burrows Wheeler Markov Distance (BWMD) as an alternative. The BWMD avoids the shortcomings of earlier efforts, and allows us to tackle problems in variable length DNA sequence clustering. BWMD is also more adaptable to other domains, which we demonstrate on malware classification tasks. Unlike other compression-based distance metrics known to us, BWMD works by embedding sequences into a fixed-length feature vector. This allows us to provide significantly improved clustering performance on larger malware corpora, a weakness of prior methods.

scholarly journals Combined Use of k-Mer Numerical Features and Position-Specific Categorical Features in Fixed-Length DNA Sequence Classification

2017 ◽  
Vol 10 (08) ◽  
pp. 390-401 ◽  
Author(s):  
Dau Phan ◽  
Ngoc Giang Nguyen ◽  
Favorisen Rosyking Lumbanraja ◽  
Mohammad Reza Faisal ◽  
Bahriddin Abapihi ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Sequence Classification ◽  
Fixed Length ◽  
Combined Use

scholarly journals Graph diffusion distance: Properties and efficient computation

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249624
Author(s):  
C. B. Scott ◽  
Eric Mjolsness
Keyword(s):  
Distance Measure ◽  
Fixed Time ◽  
Graph Laplacian ◽  
Upper Bounds ◽  
Distance Measures ◽  
Distance Metrics ◽  
Graph Products ◽  
Efficient Computation ◽  
New Family ◽  
Sparsity Constraints

We define a new family of similarity and distance measures on graphs, and explore their theoretical properties in comparison to conventional distance metrics. These measures are defined by the solution(s) to an optimization problem which attempts find a map minimizing the discrepancy between two graph Laplacian exponential matrices, under norm-preserving and sparsity constraints. Variants of the distance metric are introduced to consider such optimized maps under sparsity constraints as well as fixed time-scaling between the two Laplacians. The objective function of this optimization is multimodal and has discontinuous slope, and is hence difficult for univariate optimizers to solve. We demonstrate a novel procedure for efficiently calculating these optima for two of our distance measure variants. We present numerical experiments demonstrating that (a) upper bounds of our distance metrics can be used to distinguish between lineages of related graphs; (b) our procedure is faster at finding the required optima, by as much as a factor of 103; and (c) the upper bounds satisfy the triangle inequality exactly under some assumptions and approximately under others. We also derive an upper bound for the distance between two graph products, in terms of the distance between the two pairs of factors. Additionally, we present several possible applications, including the construction of infinite “graph limits” by means of Cauchy sequences of graphs related to one another by our distance measure.

scholarly journals Distance Metrics of D Numbers

2020 ◽  
Author(s):  
Liguo Fei ◽  
Yuqiang Feng
Keyword(s):  
Number Theory ◽  
Incomplete Information ◽  
Distance Function ◽  
Distance Measure ◽  
Complete Information ◽  
Belief Function ◽  
Distance Metrics ◽  
Modeling Methods ◽  
The Difference ◽  
D Numbers

Belief function has always played an indispensable role in modeling cognitive uncertainty. As an inherited version, the theory of D numbers has been proposed and developed in a more efficient and robust way. Within the framework of D number theory, two more generalized properties are extended: (1) the elements in the frame of discernment (FOD) of D numbers do not required to be mutually exclusive strictly; (2) the completeness constraint is released. The investigation shows that the distance function is very significant in measuring the difference between two D numbers, especially in information fusion and decision. Modeling methods of uncertainty that incorporate D numbers have become increasingly popular, however, very few approaches have tackled the challenges of distance metrics. In this study, the distance measure of two D numbers is presented in cases, including complete information, incomplete information, and non-exclusive elements

scholarly journals Improving Topic Models with Latent Feature Word Representations

2015 ◽  
Vol 3 ◽  
pp. 299-313 ◽  
Author(s):  
Dat Quoc Nguyen ◽  
Richard Billingsley ◽  
Lan Du ◽  
Mark Johnson
Keyword(s):  
High Performance ◽  
Feature Vector ◽  
Topic Models ◽  
Document Collections ◽  
Probabilistic Topic Models ◽  
New Models ◽  
Classification Tasks ◽  
Latent Topics ◽  
Vector Representations ◽  
Feature Word

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.

Image Classification Based on Conditional Random Fields

2014 ◽  
Vol 556-562 ◽  
pp. 4901-4905
Author(s):  
Yang Yan ◽  
Wen Bo Huang ◽  
Yun Ji Wang
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Feature Vector ◽  
Classification Task ◽  
Spatial Dependencies ◽  
Classification Tasks ◽  
Task Feature ◽  
Vector Modeling ◽  
Multi Classification ◽  
Original Feature

We use Conditional Random Fields (CRFs) to classify regions in an image. CRFs provide a discriminative framework to incorporate spatial dependencies in an image, which is more appropriate for classification tasks as opposed to a generative framework. In this paper we apply CRFs to the image multi-classification task, we focus on three aspects of the classification task: feature extraction, the Original feature clustering based on K-means, and feature vector modeling base on CRF to obtain multiclass classification. We present classification results on sample images from Cambridge (MSRC) database, and the experimental results show that the method we present can classify the images accurately.

scholarly journals An Optimal Seed Based Compression Algorithm for DNA Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Pamela Vinitha Eric ◽  
Gopakumar Gopalakrishnan ◽  
Muralikrishnan Karunakaran
Keyword(s):  
Dna Sequence ◽  
Dna Sequences ◽  
Lossless Compression ◽  
Compression Algorithm ◽  
Compression Scheme ◽  
Substitution Method ◽  
Compression Algorithms ◽  
Dna Sequence Compression ◽  
Sequence Compression ◽  
Better Than

This paper proposes a seed based lossless compression algorithm to compress a DNA sequence which uses a substitution method that is similar to the LempelZiv compression scheme. The proposed method exploits the repetition structures that are inherent in DNA sequences by creating an offline dictionary which contains all such repeats along with the details of mismatches. By ensuring that only promising mismatches are allowed, the method achieves a compression ratio that is at par or better than the existing lossless DNA sequence compression algorithms.

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