May 6, 2019

3086 words 15 mins read

Paper Group ANR 344

Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community. Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis. Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis. Domain Adaptation with L2 constraints for …

Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community


Title	Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community
Authors	Ethan Fast, Tina Vachovsky, Michael S. Bernstein
Abstract	Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a generation increasingly aware of social constructs like sexism and racism? Do these stories tend to reinforce gender stereotypes, or counter them? In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction. We apply this technique across 1.8 billion words of fiction from the Wattpad online writing community, investigating gender representation in stories, how male and female characters behave and are described, and how authors’ use of gender stereotypes is associated with the community’s ratings. We find that male over-representation and traditional gender stereotypes (e.g., dominant men and submissive women) are common throughout nearly every genre in our corpus. However, only some of these stereotypes, like sexual or violent men, are associated with highly rated stories. Finally, despite women often being the target of negative stereotypes, female authors are equally likely to write such stereotypes as men.
Tasks
Published	2016-03-29
URL	http://arxiv.org/abs/1603.08832v1
PDF	http://arxiv.org/pdf/1603.08832v1.pdf
PWC	https://paperswithcode.com/paper/shirtless-and-dangerous-quantifying
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Framework

Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis


Title	Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis
Authors	Md Ashad Alam, Yu-Ping Wang
Abstract	Imaging genetic research has essentially focused on discovering unique and co-association effects, but typically ignoring to identify outliers or atypical objects in genetic as well as non-genetics variables. Identifying significant outliers is an essential and challenging issue for imaging genetics and multiple sources data analysis. Therefore, we need to examine for transcription errors of identified outliers. First, we address the influence function (IF) of kernel mean element, kernel covariance operator, kernel cross-covariance operator, kernel canonical correlation analysis (kernel CCA) and multiple kernel CCA. Second, we propose an IF of multiple kernel CCA, which can be applied for more than two datasets. Third, we propose a visualization method to detect influential observations of multiple sources of data based on the IF of kernel CCA and multiple kernel CCA. Finally, the proposed methods are capable of analyzing outliers of subjects usually found in biomedical applications, in which the number of dimension is large. To examine the outliers, we use the stem-and-leaf display. Experiments on both synthesized and imaging genetics data (e.g., SNP, fMRI, and DNA methylation) demonstrate that the proposed visualization can be applied effectively.
Tasks
Published	2016-06-01
URL	http://arxiv.org/abs/1606.00113v1
PDF	http://arxiv.org/pdf/1606.00113v1.pdf
PWC	https://paperswithcode.com/paper/identifying-outliers-using-influence-function
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Framework

Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis


Title	Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis
Authors	Md ashad Alam, Osamu Komori, Yu-Ping Wang
Abstract	In genome-wide interaction studies, to detect gene-gene interactions, most methods are divided into two folds: single nucleotide polymorphisms (SNP) based and gene-based methods. Basically, the methods based on the gene are more effective than the methods based on a single SNP. Recent years, while the kernel canonical correlation analysis (Classical kernel CCA) based U statistic (KCCU) has proposed to detect the nonlinear relationship between genes. To estimate the variance in KCCU, they have used resampling based methods which are highly computationally intensive. In addition, classical kernel CCA is not robust to contaminated data. We, therefore, first discuss robust kernel mean element, the robust kernel covariance, and cross-covariance operators. Second, we propose a method based on influence function to estimate the variance of the KCCU. Third, we propose a nonparametric robust KCCU method based on robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA. Finally, we investigate the proposed methods to synthesized data and imaging genetic data set. Based on gene ontology and pathway analysis, the synthesized and genetics analysis demonstrate that the proposed robust method shows the superior performance of the state-of-the-art methods.
Tasks
Published	2016-06-01
URL	http://arxiv.org/abs/1606.00118v1
PDF	http://arxiv.org/pdf/1606.00118v1.pdf
PWC	https://paperswithcode.com/paper/gene-gene-association-for-imaging-genetics
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Domain Adaptation with L2 constraints for classifying images from different endoscope systems


Title	Domain Adaptation with L2 constraints for classifying images from different endoscope systems
Authors	Toru Tamaki, Shoji Sonoyama, Takio Kurita, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka, Kazuaki Chayama
Abstract	This paper proposes a method for domain adaptation that extends the maximum margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2 distance constraints between samples of different domains; thus, our method is denoted as MMDTL2. Motivated by the differences between the images taken by narrow band imaging (NBI) endoscopic devices, we utilize different NBI devices as different domains and estimate the transformations between samples of different domains, i.e., image samples taken by different NBI endoscope systems. We first formulate the problem in the primal form, and then derive the dual form with much lesser computational costs as compared to the naive approach. From our experimental results using NBI image datasets from two different NBI endoscopic devices, we find that MMDTL2 is better than MMDT and also support vector machines without adaptation, especially when NBI image features are high-dimensional and the per-class training samples are greater than 20.
Tasks	Domain Adaptation
Published	2016-11-08
URL	http://arxiv.org/abs/1611.02443v2
PDF	http://arxiv.org/pdf/1611.02443v2.pdf
PWC	https://paperswithcode.com/paper/domain-adaptation-with-l2-constraints-for
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Framework

Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering


Title	Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering
Authors	Xiao Fu, Kejun Huang, Bo Yang, Wing-Kin Ma, Nicholas D. Sidiropoulos
Abstract	This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF). VolMin is a factorization criterion that decomposes a given data matrix into a basis matrix times a structured coefficient matrix via finding the minimum-volume simplex that encloses all the columns of the data matrix. Recent work showed that VolMin guarantees the identifiability of the factor matrices under mild conditions that are realistic in a wide variety of applications. This paper focuses on both theoretical and practical aspects of VolMin. On the theory side, exact equivalence of two independently developed sufficient conditions for VolMin identifiability is proven here, thereby providing a more comprehensive understanding of this aspect of VolMin. On the algorithm side, computational complexity and sensitivity to outliers are two key challenges associated with real-world applications of VolMin. These are addressed here via a new VolMin algorithm that handles volume regularization in a computationally simple way, and automatically detects and {iteratively downweights} outliers, simultaneously. Simulations and real-data experiments using a remotely sensed hyperspectral image and the Reuters document corpus are employed to showcase the effectiveness of the proposed algorithm.
Tasks
Published	2016-08-15
URL	http://arxiv.org/abs/1608.04290v1
PDF	http://arxiv.org/pdf/1608.04290v1.pdf
PWC	https://paperswithcode.com/paper/robust-volume-minimization-based-matrix
Repo
Framework

Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions


Title	Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions
Authors	Lin Wu, Yang Wang
Abstract	Learning hash functions/codes for similarity search over multi-view data is attracting increasing attention, where similar hash codes are assigned to the data objects characterizing consistently neighborhood relationship across views. Traditional methods in this category inherently suffer three limitations: 1) they commonly adopt a two-stage scheme where similarity matrix is first constructed, followed by a subsequent hash function learning; 2) these methods are commonly developed on the assumption that data samples with multiple representations are noise-free,which is not practical in real-life applications; 3) they often incur cumbersome training model caused by the neighborhood graph construction using all $N$ points in the database ($O(N)$). In this paper, we motivate the problem of jointly and efficiently training the robust hash functions over data objects with multi-feature representations which may be noise corrupted. To achieve both the robustness and training efficiency, we propose an approach to effectively and efficiently learning low-rank kernelized \footnote{We use kernelized similarity rather than kernel, as it is not a squared symmetric matrix for data-landmark affinity matrix.} hash functions shared across views. Specifically, we utilize landmark graphs to construct tractable similarity matrices in multi-views to automatically discover neighborhood structure in the data. To learn robust hash functions, a latent low-rank kernel function is used to construct hash functions in order to accommodate linearly inseparable data. In particular, a latent kernelized similarity matrix is recovered by rank minimization on multiple kernel-based similarity matrices. Extensive experiments on real-world multi-view datasets validate the efficacy of our method in the presence of error corruptions.
Tasks	graph construction
Published	2016-11-17
URL	http://arxiv.org/abs/1611.05521v1
PDF	http://arxiv.org/pdf/1611.05521v1.pdf
PWC	https://paperswithcode.com/paper/robust-hashing-for-multi-view-data-jointly
Repo
Framework

Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches


Title	Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches
Authors	Dongwoo Kim, Lexing Xie, Cheng Soon Ong
Abstract	Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinder the subsequent statistical inference. The gap between these two processes can be reduced by an incremental population approach. We propose a new probabilistic knowledge graph factorisation method that benefits from the path structure of existing knowledge (e.g. syllogism) and enables a common modelling approach to be used for both incremental population and knowledge completion tasks. More specifically, the probabilistic formulation allows us to develop an incremental population algorithm that trades off exploitation-exploration. Experiments on three benchmark datasets show that the balanced exploitation-exploration helps the incremental population, and the additional path structure helps to predict missing information in knowledge completion.
Tasks	graph construction
Published	2016-08-21
URL	http://arxiv.org/abs/1608.05921v2
PDF	http://arxiv.org/pdf/1608.05921v2.pdf
PWC	https://paperswithcode.com/paper/probabilistic-knowledge-graph-construction
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Framework

Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model


Title	Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model
Authors	Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Olena O. Boiko
Abstract	An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.
Tasks	Time Series, Time Series Forecasting
Published	2016-10-20
URL	http://arxiv.org/abs/1610.06486v1
PDF	http://arxiv.org/pdf/1610.06486v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-forecasting-of-non-stationary
Repo
Framework

SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval


Title	SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval
Authors	Jian Zhang, Yuxin Peng
Abstract	Hashing methods have been widely used for efficient similarity retrieval on large scale image database. Traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted features cannot optimally represent the image content and preserve the semantic similarity. Recently, several deep hashing methods have shown better performance because the deep architectures generate more discriminative feature representations. However, these deep hashing methods are mainly designed for supervised scenarios, which only exploit the semantic similarity information, but ignore the underlying data structures. In this paper, we propose the semi-supervised deep hashing (SSDH) approach, to perform more effective hash function learning by simultaneously preserving semantic similarity and underlying data structures. The main contributions are as follows: (1) We propose a semi-supervised loss to jointly minimize the empirical error on labeled data, as well as the embedding error on both labeled and unlabeled data, which can preserve the semantic similarity and capture the meaningful neighbors on the underlying data structures for effective hashing. (2) A semi-supervised deep hashing network is designed to extensively exploit both labeled and unlabeled data, in which we propose an online graph construction method to benefit from the evolving deep features during training to better capture semantic neighbors. To the best of our knowledge, the proposed deep network is the first deep hashing method that can perform hash code learning and feature learning simultaneously in a semi-supervised fashion. Experimental results on 5 widely-used datasets show that our proposed approach outperforms the state-of-the-art hashing methods.
Tasks	graph construction, Image Retrieval, Semantic Similarity, Semantic Textual Similarity
Published	2016-07-28
URL	http://arxiv.org/abs/1607.08477v3
PDF	http://arxiv.org/pdf/1607.08477v3.pdf
PWC	https://paperswithcode.com/paper/ssdh-semi-supervised-deep-hashing-for-large
Repo
Framework

Orthogonal Random Features


Title	Orthogonal Random Features
Authors	Felix X. Yu, Ananda Theertha Suresh, Krzysztof Choromanski, Daniel Holtmann-Rice, Sanjiv Kumar
Abstract	We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error. We call this technique Orthogonal Random Features (ORF), and provide theoretical and empirical justification for this behavior. Motivated by this discovery, we further propose Structured Orthogonal Random Features (SORF), which uses a class of structured discrete orthogonal matrices to speed up the computation. The method reduces the time cost from $\mathcal{O}(d^2)$ to $\mathcal{O}(d \log d)$, where $d$ is the data dimensionality, with almost no compromise in kernel approximation quality compared to ORF. Experiments on several datasets verify the effectiveness of ORF and SORF over the existing methods. We also provide discussions on using the same type of discrete orthogonal structure for a broader range of applications.
Tasks
Published	2016-10-28
URL	http://arxiv.org/abs/1610.09072v1
PDF	http://arxiv.org/pdf/1610.09072v1.pdf
PWC	https://paperswithcode.com/paper/orthogonal-random-features
Repo
Framework

Graph Construction with Label Information for Semi-Supervised Learning


Title	Graph Construction with Label Information for Semi-Supervised Learning
Authors	Liansheng Zhuang, Zihan Zhou, Jingwen Yin, Shenghua Gao, Zhouchen Lin, Yi Ma, Nenghai Yu
Abstract	In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR). This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real datasets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.
Tasks	graph construction
Published	2016-07-08
URL	http://arxiv.org/abs/1607.02539v3
PDF	http://arxiv.org/pdf/1607.02539v3.pdf
PWC	https://paperswithcode.com/paper/graph-construction-with-label-information-for
Repo
Framework

An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes


Title	An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes
Authors	Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Olena O. Boiko
Abstract	Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.
Tasks	Time Series, Time Series Forecasting
Published	2016-10-20
URL	http://arxiv.org/abs/1610.06484v1
PDF	http://arxiv.org/pdf/1610.06484v1.pdf
PWC	https://paperswithcode.com/paper/an-evolving-cascade-system-based-on-a-set-of
Repo
Framework

ChoiceRank: Identifying Preferences from Node Traffic in Networks


Title	ChoiceRank: Identifying Preferences from Node Traffic in Networks
Authors	Lucas Maystre, Matthias Grossglauser
Abstract	Understanding how users navigate in a network is of high interest in many applications. We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities. We cast it as a preference learning problem, and we study a model where choices follow Luce’s axiom. In this case, the $O(n)$ marginal counts of node visits are a sufficient statistic for the $O(n^2)$ transition probabilities. We show how to make the inference problem well-posed regardless of the network’s structure, and we present ChoiceRank, an iterative algorithm that scales to networks that contains billions of nodes and edges. We apply the model to two clickstream datasets and show that it successfully recovers the transition probabilities using only the network structure and marginal (node-level) traffic data. Finally, we also consider an application to mobility networks and apply the model to one year of rides on New York City’s bicycle-sharing system.
Tasks
Published	2016-10-20
URL	http://arxiv.org/abs/1610.06525v2
PDF	http://arxiv.org/pdf/1610.06525v2.pdf
PWC	https://paperswithcode.com/paper/choicerank-identifying-preferences-from-node
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Framework

On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities


Title	On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Authors	Alexander Hagg, Frederik Hegger, Paul Plöger
Abstract	Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios. We show that a combination of cues from multiple sensor modalities, including specular reflectance and unavailable depth information, allows us to capture a larger subset of household objects by extending a state of the art object recognition method. This leads to a significant increase in robustness of recognition over a larger set of commonly used objects.
Tasks	Object Recognition
Published	2016-06-03
URL	http://arxiv.org/abs/1606.01001v1
PDF	http://arxiv.org/pdf/1606.01001v1.pdf
PWC	https://paperswithcode.com/paper/on-recognizing-transparent-objects-in
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Framework

Interpretable Machine Learning Models for the Digital Clock Drawing Test


Title	Interpretable Machine Learning Models for the Digital Clock Drawing Test
Authors	William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, Dana Penney
Abstract	The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possible the analysis of both the drawing process and final product. We developed methodology to analyze pen stroke data from these drawings, and computed a large collection of features which were then analyzed with a variety of machine learning techniques. The resulting scoring systems were designed to be more accurate than the systems currently used by clinicians, but just as interpretable and easy to use. The systems also allow us to quantify the tradeoff between accuracy and interpretability. We created automated versions of the CDT scoring systems currently used by clinicians, allowing us to benchmark our models, which indicated that our machine learning models substantially outperformed the existing scoring systems.
Tasks	Interpretable Machine Learning
Published	2016-06-23
URL	http://arxiv.org/abs/1606.07163v1
PDF	http://arxiv.org/pdf/1606.07163v1.pdf
PWC	https://paperswithcode.com/paper/interpretable-machine-learning-models-for-the
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