manifold

Exploring the beauty of mapping between spaces in SVMs, autoencoders, and manifold learning (isomaps) algorithmsPhoto by Evgeni Tcherkasski on UnsplashIntroductionIn machine learning, understanding how algorithms process, interpret, and classify data relies heavily on the concept of “spaces.” In this context, a space is a mathematical construct where data points are positioned based on their features. Each dimension in the space represents a specific attribute or feature of the data, allowing algorithms to navigate a…

Illustration of fitting the latent manifold using the Cycle Generative Adversarial Network (CycleGAN). CycleGAN is a deep learning technique for unsupervised image-to-image translation. In the real world, data, such as the images shown in panel (a), are often high-dimensional vectors. These vectors typically reside around a low-dimensional latent manifold, depicted by the black dotted curve in panel (b). The CycleGAN framework, detailed in panel (c),…

Unravelling Complexity: A Novel Approach to Manifold Learning Using Noise InjectionIn the world of data science, high-dimensional data presents both a challenge and opportunity. Whilst it provides a treasure trove of relationships and patterns that can be moulded and transformed, without careful cleaning and selection it can become overwhelming to analyse and draw conclusions from: “The curse of dimensionality”. Whilst instinctively you may lean to Principal Component Analysis to embed the data to a smaller subspace, you…

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PTI New Delhi, November 19 The frequency of extreme weather events such as floods and heat waves is projected to rise manifold in India in the future due to climate change, according to a study by researchers at the Indian Institute of Technology, Gandhinagar. The research team studied the period from 1951 to 2020 to quantify India’s risk of sequential extremes – heat waves in the summer and extreme rain in the following summer…