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Multiple kernel learning

About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.


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Journal ArticleDOI
03 Oct 2018
TL;DR: A novel approach of daily activity recognition is proposed and it is hypothesized that the performance of the system can be promoted by combining multimodal features, and results prove the proposed methods are effective and feasible for activity recognition system in the daily environment.
Abstract: Introduction: Recognizing human activity in a daily environment has attracted much research in computer vision and recognition in recent years. It is a difficult and challenging topic not only inasmuch as the variations of background clutter, occlusion or intra-class variation in image sequences but also inasmuch as complex patterns of activity are created by interactions among people-people or people-objects. In addition, it also is very valuable for many practical applications, such as smart home, gaming, health care, human-computer interaction and robotics. Now, we are living in the beginning age of the industrial revolution 4.0 where intelligent systems have become the most important subject, as reflected in the research and industrial communities. There has been emerging advances in 3D cameras, such as Microsoft's Kinect and Intel's RealSense, which can capture RGB, depth and skeleton in real time. This creates a new opportunity to increase the capabilities of recognizing the human activity in the daily environment. In this research, we propose a novel approach of daily activity recognition and hypothesize that the performance of the system can be promoted by combining multimodal features. Methods: We extract spatial-temporal feature for the human body with representation of parts based on skeleton data from RGB-D data. Then, we combine multiple features from the two sources to yield the robust features for activity representation. Finally, we use the Multiple Kernel Learning algorithm to fuse multiple features to identify the activity label for each video. To show generalizability, the proposed framework has been tested on two challenging datasets by cross-validation scheme. Results: The experimental results show a good outcome on both CAD120 and MSR-Daily Activity 3D datasets with 94.16% and 95.31% in accuracy, respectively. Conclusion: These results prove our proposed methods are effective and feasible for activity recognition system in the daily environment.

2 citations

Book ChapterDOI
19 Jul 2020
TL;DR: In this article, a generalized quadratic loss for learning problems that examines pattern correlations in order to concentrate the learning problem into input space regions where patterns are more densely distributed is proposed.
Abstract: We consider some supervised binary classification tasks and a regression task, whereas SVM and Deep Learning, at present, exhibit the best generalization performances. We extend the work [3] on a generalized quadratic loss for learning problems that examines pattern correlations in order to concentrate the learning problem into input space regions where patterns are more densely distributed. From a shallow methods point of view (e.g.: SVM), since the following mathematical derivation of problem (9) in [3] is incorrect, we restart from problem (8) in [3] and we try to solve it with one procedure that iterates over the dual variables until the primal and dual objective functions converge. In addition we propose another algorithm that tries to solve the classification problem directly from the primal problem formulation. We make also use of Multiple Kernel Learning to improve generalization performances. Moreover, we introduce for the first time a custom loss that takes in consideration pattern correlation for a shallow and a Deep Learning task. We propose some pattern selection criteria and the results on 4 UCI data-sets for the SVM method. We also report the results on a larger binary classification data-set based on Twitter, again drawn from UCI, combined with shallow Learning Neural Networks, with and without the generalized quadratic loss. At last, we test our loss with a Deep Neural Network within a larger regression task taken from UCI. We compare the results of our optimizers with the well known solver \(\text {SVM}^{\text {light}}\) and with Keras Multi-Layers Neural Networks with standard losses and with a parameterized generalized quadratic loss, and we obtain comparable results (Code is available at: https://osf.io/fbzsc/wiki/home/).

2 citations

Proceedings ArticleDOI
12 Sep 2011
TL;DR: A multiple kernel learning (MKL) method to integrate multiple Gaussian distance kernels to further improve time series classification accuracy and results show that the proposed method is superior to SVM with individual Gaussiandistance kernel.
Abstract: Various distance measures have been proposed for time series classification, and several of them have been used to construct Gaussian distance kernels for support vector machine (SVM) - based classification. Considering that different Gaussian distance kernels may carry complementary information for classification, in this paper, we propose a multiple kernel learning (MKL) method to integrate multiple Gaussian distance kernels to further improve time series classification accuracy. We first adopt the classical Gaussian RBF (GRBF) kernel and the recently developed Gaussian elastic metric distance kernel (i.e. GERP kernel and GTWED kernel), and then use an efficient MKL, SimpleMKL, to learn the kernel classifier. Our experimental results on 12 UCR time series data sets show that the proposed method is superior to SVM with individual Gaussian distance kernel.

2 citations

Dissertation
Hao Fu1
13 Dec 2012
TL;DR: A novel way to utilize the random forest is proposed and the concept of semantic nearest neighbor and semantic similarity measure is proposed based on these two concepts, and novel methods for image annotation and image retrieval tasks are devised.
Abstract: The aim of semantic image understanding is to reveal the semantic meaning behind the image pixel. This thesis investigates problems related to semantic image understanding, and have made the following contributions. Our first contribution is to propose the usage of histogram matching in Multiple Kernel Learning. We treat the two-dimensional kernel matrix as an image and transfer the histogram matching algorithm in image processing to kernel matrix. Experiments on various computer vision and machine learning datasets have shown that our method can always boost the performance of state of the art MKL methods. Our second contribution is to advocate the segment-then-recognize strategy in pixel-level semantic image understanding. We have developed a new framework which tries to integrate semantic segmentation with low-level segmentation for proposing object consistent regions. We have also developed a novel method trying to integrate semantic segmentation with interactive segmentation. We found this segment-then-recognize strategy also works well on medical image data, where we designed a novel polar space random field model for proposing gland-like regions. In the realm of image-level semantic image understanding, our contribution is a novel way to utilize the random forest. Most of the previous works utilizing random forest store the posterior probabilities at each leaf node, and each random tree in the random forest is considered to be independent from each other. In contrast, we store the training samples instead of the posterior probabilities at each leaf node. We consider the random forest as a whole and propose the concept of semantic nearest neighbor and semantic similarity measure. Based on these two concepts, we devise novel methods for image annotation and image retrieval tasks.

2 citations

Book ChapterDOI
01 Oct 2012
TL;DR: A Clustered Localized Multiple Kernel Learning (CLMKL) algorithm is proposed by encoding in the classication model the information on the clusters of apriory known stratifications by exploiting the knowledge on heterogeneity factors to improve the classification accuracy.
Abstract: Automatic decisional systems based on pattern classification methods are becoming very important to support medical diagnosis. In general, the overall objective is to classify between healthy subjects and patients affected by a certain disease. To reach this aim, significant efforts have been spent in finding reliable biomarkers which are able to robustly discriminate between the two populations (i.e., patients and controls). However, in real medical scenarios there are many factors, like the gender or the age, which make the source data very heterogeneous. This introduces a large intra-class variation by affecting the performance of the classification procedure. In this paper we exploit how to use the knowledge on heterogeneity factors to improve the classification accuracy. We propose a Clustered Localized Multiple Kernel Learning (CLMKL) algorithm by encoding in the classication model the information on the clusters of apriory known stratifications.

2 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202321
202244
202172
2020101
2019113
2018114