25 research outputs found
Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction
© 2015 IEEE. Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multi-view data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features. Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the main problem of multi-view canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained. In addition, a non-linear extension of TCCA is presented. Experiments on various challenge tasks, including large scale biometric structure prediction, internet advertisement classification, and web image annotation, demonstrate the effectiveness of the proposed method
ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment
Recruitment of appropriate people for certain positions is critical for any
companies or organizations. Manually screening to select appropriate candidates
from large amounts of resumes can be exhausted and time-consuming. However,
there is no public tool that can be directly used for automatic resume quality
assessment (RQA). This motivates us to develop a method for automatic RQA.
Since there is also no public dataset for model training and evaluation, we
build a dataset for RQA by collecting around 10K resumes, which are provided by
a private resume management company. By investigating the dataset, we identify
some factors or features that could be useful to discriminate good resumes from
bad ones, e.g., the consistency between different parts of a resume. Then a
neural-network model is designed to predict the quality of each resume, where
some text processing techniques are incorporated. To deal with the label
deficiency issue in the dataset, we propose several variants of the model by
either utilizing the pair/triplet-based loss, or introducing some
semi-supervised learning technique to make use of the abundant unlabeled data.
Both the presented baseline model and its variants are general and easy to
implement. Various popular criteria including the receiver operating
characteristic (ROC) curve, F-measure and ranking-based average precision (AP)
are adopted for model evaluation. We compare the different variants with our
baseline model. Since there is no public algorithm for RQA, we further compare
our results with those obtained from a website that can score a resume.
Experimental results in terms of different criteria demonstrate the
effectiveness of the proposed method. We foresee that our approach would
transform the way of future human resources management.Comment: ICD
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In hyperspectral remote sensing data mining, it is important to take into
account of both spectral and spatial information, such as the spectral
signature, texture feature and morphological property, to improve the
performances, e.g., the image classification accuracy. In a feature
representation point of view, a nature approach to handle this situation is to
concatenate the spectral and spatial features into a single but high
dimensional vector and then apply a certain dimension reduction technique
directly on that concatenated vector before feed it into the subsequent
classifier. However, multiple features from various domains definitely have
different physical meanings and statistical properties, and thus such
concatenation hasn't efficiently explore the complementary properties among
different features, which should benefit for boost the feature
discriminability. Furthermore, it is also difficult to interpret the
transformed results of the concatenated vector. Consequently, finding a
physically meaningful consensus low dimensional feature representation of
original multiple features is still a challenging task. In order to address the
these issues, we propose a novel feature learning framework, i.e., the
simultaneous spectral-spatial feature selection and extraction algorithm, for
hyperspectral images spectral-spatial feature representation and
classification. Specifically, the proposed method learns a latent low
dimensional subspace by projecting the spectral-spatial feature into a common
feature space, where the complementary information has been effectively
exploited, and simultaneously, only the most significant original features have
been transformed. Encouraging experimental results on three public available
hyperspectral remote sensing datasets confirm that our proposed method is
effective and efficient
Data Fusion for MaaS: Opportunities and Challenges
© 2018 IEEE. Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling
An efficient data masking for securing medical data using DNA encoding and chaotic system
Data security is utmost important for ubiquitous computing of medical/diagnostic data or images. Along with must consider preserving privacy of patients. Recently, deoxyribose nucleic acid (DNA) sequences and chaotic sequence are jointly used for building efficient data masking model. However, the state-of-art model are not robust against noise and cropping attack (CA). Since in existing model most digits of each pixel are not altered. This work present efficient data masking (EDM) method using chaos and DNA based encryption method for securing health care data. For overcoming research challenges effective bit scrambling method is required. Firstly, this work present an efficient bit scrambling using logistic sine map and pseudorandom sequence using chaotic system. Then, DNA substitution is performed among them to resist against differential attack (DA), statistical attack (SA) and CA. Experiment are conducted on standard considering diverse images. The outcome achieved shows proposed model efficient when compared to existing models