Category Archives: Machine Learning

A clustering algorithm that claims to be simpler and faster than others

June 14, 2024 14:33 ,

Yewang Chen, Yuanyuan Yang, Songwen Pei, Yi Chen, Jixiang Du, A simple rapid sample-based clustering for large-scale data, Engineering Applications of Artificial Intelligence, Volume 133, Part F, 2024 DOI: 10.1016/j.engappai.2024.108551.

Large-scale data clustering is a crucial task in addressing big data challenges. However, existing approaches often struggle to efficiently and effectively identify different types of big data, making it a significant challenge. In this paper, we propose a novel sample-based clustering algorithm, which is very simple but extremely efficient, and runs in about O(n×r) expected time, where n is the size of the dataset and r is the category number. The method is based on two key assumptions: (1) The data of each sufficient sample should have similar data distribution, as well as category distribution, to the entire data set; (2) the representative of each category in all sufficient samples conform to Gaussian distribution. It processes data in two stages, one is to classify data in each local sample independently, and the other is to globally classify data by assigning each point to the category of its nearest representative category center. The experimental results show that the proposed algorithm is effective, which outperforms other current variants of clustering algorithm.

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Estimating aging of integrated circuits with machine learning

October 20, 2020 19:05 ,

Ke Huang; Xinqiao Zhang; Naghmeh Karimi, Real-Time Prediction for IC Aging Based on Machine Learning, IEEE Transactions on Instrumentation and Measurement, Volume: 68, Issue: 12, Dec. 2019, DOI: 10.1109/TIM.2019.2899477.

Estimating the aging-related degradation and failure of nanoscale integrated circuits (ICs), before they actually occur, is crucial for developing aging prevention/mitigation actions and in turn avoiding unexpected in-field circuit failures. Real-time monitoring of IC operating conditions can be efficiently used for predicting aging degradation and in turn timing failures caused by device aging. The existing approaches only take some specific operating conditions (e.g., workload or temperature) into account. In this paper, we propose a novel method for real-time IC aging prediction by extending the prediction schemes to a comprehensive model which takes into account any time-variant dynamic operating conditions relevant to aging prediction. Using a machine learning prediction model and the notion of equivalent aging time, we show that our approach outperforms the existing methods in terms of aging-prediction accuracy under different scenarios of time-variant operating conditions.

Posted in: Electronics, Machine learning

An interesting review of criticisms of deep learning in cognitive science

March 20, 2019 10:22 ,

Radoslaw M. Cichy, Daniel Kaiser, Deep Neural Networks as Scientific Models, Trends in Cognitive Sciences, Volume 23, Issue 4, 2019, Pages 305-317, DOI: 10.1016/j.tics.2019.01.009.

Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to solve cognitive tasks at which humans excel. In the absence of explanations for such cognitive phenomena, in turn cognitive scientists have started using DNNs as models to investigate biological cognition and its neural basis, creating heated debate. Here, we reflect on the case from the perspective of philosophy of science. After putting DNNs as scientific models into context, we discuss how DNNs can fruitfully contribute to cognitive science. We claim that beyond their power to provide predictions and explanations of cognitive phenomena, DNNs have the potential to contribute to an often overlooked but ubiquitous and fundamental use of scientific models: exploration.

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A new method of clustering of data with many advantages w.r.t. others

June 22, 2017 08:44 ,

A. Sharma, K. A. Boroevich, D. Shigemizu, Y. Kamatani, M. Kubo and T. Tsunoda, “Hierarchical Maximum Likelihood Clustering Approach,” in IEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 112-122, Jan. 2017. DOI: 10.1109/TBME.2016.2542212.

In this paper, we focused on developing a clustering approach for biological data. In many biological analyses, such as multiomics data analysis and genome-wide association studies analysis, it is crucial to find groups of data belonging to subtypes of diseases or tumors. Methods: Conventionally, the k-means clustering algorithm is overwhelmingly applied in many areas including biological sciences. There are, however, several alternative clustering algorithms that can be applied, including support vector clustering. In this paper, taking into consideration the nature of biological data, we propose a maximum likelihood clustering scheme based on a hierarchical framework. Results: This method can perform clustering even when the data belonging to different groups overlap. It can also perform clustering when the number of samples is lower than the data dimensionality. Conclusion: The proposed scheme is free from selecting initial settings to begin the search process. In addition, it does not require the computation of the first and second derivative of likelihood functions, as is required by many other maximum likelihood-based methods. Significance: This algorithm uses distribution and centroid information to cluster a sample and was applied to biological data. A MATLAB implementation of this method can be downloaded from the web link http://www.riken.jp/en/research/labs/ims/med_sci_math/.

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Robust Estimation of Unbalanced Mixture Models on Samples with Outliers

October 6, 2015 08:40 ,

Galimzianova, A.; Pernus, F.; Likar, B.; Spiclin, Z., Robust Estimation of Unbalanced Mixture Models on Samples with Outliers, in Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.37, no.11, pp.2273-2285, Nov. 1 2015, DOI: 10.1109/TPAMI.2015.2404835.

Mixture models are often used to compactly represent samples from heterogeneous sources. However, in real world, the samples generally contain an unknown fraction of outliers and the sources generate different or unbalanced numbers of observations. Such unbalanced and contaminated samples may, for instance, be obtained by high density data sensors such as imaging devices. Estimation of unbalanced mixture models from samples with outliers requires robust estimation methods. In this paper, we propose a novel robust mixture estimator incorporating trimming of the outliers based on component-wise confidence level ordering of observations. The proposed method is validated and compared to the state-of-the-art FAST-TLE method on two data sets, one consisting of synthetic samples with a varying fraction of outliers and a varying balance between mixture weights, while the other data set contained structural magnetic resonance images of the brain with tumors of varying volumes. The results on both data sets clearly indicate that the proposed method is capable to robustly estimate unbalanced mixtures over a broad range of outlier fractions. As such, it is applicable to real-world samples, in which the outlier fraction cannot be estimated in advance.

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On the not-so-domain-generic nature of statistical learning in the human brain

March 11, 2015 15:09 ,

Ram Frost, Blair C. Armstrong, Noam Siegelman, Morten H. Christiansen, 2015, Domain generality versus modality specificity: the paradox of statistical learning, Trends in Cognitive Sciences, Volume 19, Issue 3, March 2015, Pages 117-125, DOI: 10.1016/j.tics.2014.12.010.

Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.

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