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Underrated but interesting machine learning concepts will be explored in this series.
There are some fascinating machine learning concepts that aren’t being talked about as much as they should be. In this article, we’ll look at a few of them, including Manifold Alignment, Quickprop, Skill Chaining, and FastICA.
The basic concepts of multiple alignment are to use the associations between instances within each data set to improve understanding of the relationships between the data sets and ultimately to map different data sets originally to the shared latent space. In several areas of machine learning and data mining, one is frequently confronted with data distributed over many dimensions. Direct handling of such high-dimensional data is often impossible, although the multiple structure on which the data is based can in many circumstances have a low intrinsic dimensionality. Chang Wang et al. states that, Multiple alignment establishes connections between two or more different data sets and enables knowledge transfer.
Jingliang Hu concluded that several techniques based on data matching (data concatenation) are less suitable for merging hyperspectral images and PolSAR data; however, multiple alignment-based approaches are better able to merge optical and SAR images. Semi-supervised methods, among the manifold alignment-based manifold techniques, they are able to use both the data structure and the existing label information efficiently.
Fahlman claims that QuickProp is a second order optimization technique that speeds optimization by simply estimating the Hessian diagonal and therefore belongs to the QuasiNewton class of algorithms. With a large number of iterations, weights, and samples, the learning process is extremely slow (or never reaches the goal). According to Windra Swastika, the quickprop approach is one way to speed up the learning process. The aim is to reduce the error gradient (E ‘). Several algorithms attempt to improve gradient descent by predicting an ideal learning rate at each update step, including AdaDelta, AdaGrad, and Adam, but QuickProp assumes the output is a quadratic function of each variable and computes the best position directly.
Windra Swastika et al. find that the quickprop approach successfully reduces the learning process compared to backpropagation. With 40 input patterns, the quickprop approach only required 20 iterations to get an error of less than 0.1, while the back-propagation method required 2000 iterations; both methods have a prediction accuracy of more than 90%.
Skill chaining is a method for discovering skills through continuous reinforcement learning. The associated deep skill chaining technique has extended them to high-dimensional continuous domains. According to Singh et al. It can be as simple as the episode’s end-of-episode event, or as complex as intrinsic events. Skills are designed so that completing each option in the chain will allow the agent to get closer to their ultimate goal by getting run another option. While skill chaining could reveal skills in continuous state spaces, it could only be used in state spaces with discrete actions that were relatively small.
George says the main benefit of skill chaining is that it takes the stress out of describing the value function of the task. Eylem Tekin concludes that strategies for chaining capabilities in different systems are useful, including those with many products and those with parallel or network flow structures such as cells with automated equipment In such situations, further research is needed to determine the potential benefits of chaining.
FastICA is a time-saving independent component analysis algorithm. Erkki Oja says the current noise-free ICA model was the inspiration for FastICA .PierreComon notes that an observed random vector is transformed into statistically independent components using Independent Component Analysis (ICA), Vicente Zarzoso said that FastICA has only been compared to neuron-based adaptive algorithms like Principal Component Analysis (PCA), which are known to outperform most ICA algorithms. The popularity of the method, in addition to its simplicity, is due to its good performance in various applications. P. Chevalier, FastICA fails with weak or highly spatially correlated sources, which is the first significant attempt.
According to Vendetta, FastICA is the most widely used method for blind source separation problems because it is computationally efficient and uses less memory than other blind source separation algorithms, such as infomax. Another advantage is that separate components can be calculated one after the other, which further reduces the calculation effort. The only drawback is that if the noise is not uniform and the noise vectors are correlated, this method cannot correctly determine the sources.