Entangled Kernels

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/358 ◽

2019 ◽

Cited By ~ 1

Author(s):

Riikka Huusari ◽

Hachem Kadri

Keyword(s):

Quantum Computing ◽

Real Data ◽

Kernel Learning ◽

Partial Trace ◽

General Family ◽

Step Algorithm ◽

New View

We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels. Borrowing tools and concepts from the field of quantum computing, such as partial trace and entanglement, we propose a new view on operator-valued kernels and define a general family of kernels that encompasses previously known operator-valued kernels, including separable and transformable kernels. Within this framework, we introduce another novel class of operator-valued kernels called entangled kernels that are not separable. We propose an efficient two-step algorithm for this framework, where the entangled kernel is learned based on a novel extension of kernel alignment to operator-valued kernels. The utility of the algorithm is illustrated on both artificial and real data.

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Multiple kernel learning for integrative consensus clustering of omic datasets

Bioinformatics ◽

10.1093/bioinformatics/btaa593 ◽

2020 ◽

Vol 36 (18) ◽

pp. 4789-4796

Author(s):

Alessandra Cabassi ◽

Paul D W Kirk

Keyword(s):

Multiple Kernel Learning ◽

Real Data ◽

Kernel Learning ◽

Supplementary Information ◽

Consensus Clustering ◽

Multiple Kernel ◽

Multiple Data ◽

Module Discovery ◽

Cluster Of Clusters ◽

R Packages

Abstract Motivation Diverse applications—particularly in tumour subtyping—have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster Of Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets is unclear. Results We rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy. KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering. This allows the contribution of noisy datasets to be down-weighted relative to more informative datasets. We compare the performances of KLIC and COCA in a variety of situations through simulation studies. We also present the output of KLIC and COCA in real data applications to cancer subtyping and transcriptional module discovery. Availability and implementation R packages klic and coca are available on the Comprehensive R Archive Network. Supplementary information Supplementary data are available at Bioinformatics online.

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Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

Sensors ◽

10.3390/s19235187 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5187 ◽

Cited By ~ 2

Author(s):

Md Nazmuzzaman Khan ◽

Sohel Anwar

Keyword(s):

Data Fusion ◽

Time Domain ◽

Confusion Matrix ◽

Object Classification ◽

Real Data ◽

Tuning Parameter ◽

Test Time ◽

Combination Rule ◽

Time Domain Data ◽

Step Algorithm

To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps( t s ).

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Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis

Jurnal Matematika ◽

10.24843/jmat.2017.v07.i01.p81 ◽

2017 ◽

Vol 7 (1) ◽

pp. 43 ◽

Cited By ~ 2

Author(s):

Rezzy Eko Caraka ◽

Hasbi Yasin ◽

Adi Waridi Basyiruddin

Keyword(s):

Palm Oil ◽

Oil Prices ◽

Feature Space ◽

Real Data ◽

Kernel Learning ◽

Support Vector ◽

Data Sets ◽

Crude Palm Oil ◽

Machine Learning Methods ◽

Radial Basis

Recently, instead of selecting a kernel has been proposed which uses SVR, where the weight of each kernel is optimized during training. Along this line of research, many pioneering kernel learning algorithms have been proposed. The use of kernels provides a powerful and principled approach to modeling nonlinear patterns through linear patterns in a feature space. Another bene?t is that the design of kernels and linear methods can be decoupled, which greatly facilitates the modularity of machine learning methods. We perform experiments on real data sets crude palm oil prices for application and better illustration using kernel radial basis. We see that evaluation gives a good to fit prediction and actual also good values showing the validity and accuracy of the realized model based on MAPE and R2. Keywords: Crude Palm Oil; Forecasting; SVR; Radial Basis; Kernel

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CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w3-195-2017 ◽

2017 ◽

Vol XLII-3/W3 ◽

pp. 195-200

Author(s):

Q. Wang ◽

Y. Gu ◽

T. Liu ◽

H. Liu ◽

X. Jin

Keyword(s):

Multiple Kernel Learning ◽

Real Data ◽

Classification Problem ◽

Kernel Learning ◽

Data Sets ◽

Specific Class ◽

Multiple Kernel ◽

Remote Sensing Image Classification ◽

Heterogeneous Features ◽

Class Pair

In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs) from multispectral image (MSI) and light detection and ranging (LiDAR) data. In particular, the <q>one-against-one</q> strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA) in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

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Machine-Learning-Based Functional Microcirculation Analysis

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i08.7044 ◽

2020 ◽

Vol 34 (08) ◽

pp. 13326-13331

Author(s):

Ossama Mahmoud ◽

GH Janssen ◽

Mahmoud R El-Sakka

Keyword(s):

Machine Learning ◽

Functional Analysis ◽

Real Data ◽

Second Step ◽

Organ Function ◽

Manual Analysis ◽

Research Task ◽

Flowing Blood ◽

Step Algorithm ◽

3D Cnn

Analysis of microcirculation is an important clinical and research task. Functional analysis of the microcirculation allows researchers to understand how blood flowing in a tissues’ smallest vessels affects disease progression, organ function, and overall health. Current methods of manual analysis of microcirculation are tedious and time-consuming, limiting the quick turnover of results. There has been limited research on automating functional analysis of microcirculation. As such, in this paper, we propose a two-step machine-learning-based algorithm to functionally assess microcirculation videos. The first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures. While the second step uses a 3D-CNN to assess whether the vessel-like structures contained flowing blood. To our knowledge, this is the first application of machine learning for functional analysis of microcirculation. We use real-world labelled microcirculation videos to train and test our algorithm and assess its performance. More precisely, we demonstrate that our two-step algorithm can efficiently analyze real data with high accuracy (90%).

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Detecting Random Responses in a Personality Scale Using IRT-Based Person-Fit Indices

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000369 ◽

2019 ◽

Vol 35 (1) ◽

pp. 126-136 ◽

Cited By ~ 2

Author(s):

Tour Liu ◽

Tian Lan ◽

Tao Xin

Keyword(s):

Experimental System ◽

Real Data ◽

Fit Indices ◽

Random Response ◽

Person Fit ◽

Index Method ◽

Single Person ◽

Person Fit Index ◽

Random Responses ◽

Response Behaviors

Abstract. Random response is a very common aberrant response behavior in personality tests and may negatively affect the reliability, validity, or other analytical aspects of psychological assessment. Typically, researchers use a single person-fit index to identify random responses. This study recommends a three-step person-fit analysis procedure. Unlike the typical single person-fit methods, the three-step procedure identifies both global misfit and local misfit individuals using different person-fit indices. This procedure was able to identify more local misfit individuals than single-index method, and a graphical method was used to visualize those particular items in which random response behaviors appear. This method may be useful to researchers in that it will provide them with more information about response behaviors, allowing better evaluation of scale administration and development of more plausible explanations. Real data were used in this study instead of simulation data. In order to create real random responses, an experimental test administration was designed. Four different random response samples were produced using this experimental system.

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