Add to ORKGIn the Data Science routine, we often face the curse of dimensionality, dealing with high-dimensional data which, in turn, can be very difficult. The problems of this nature can be approached by methods of Dimensionality Reduction. These methods assume that data can be interpreted in a smaller dimension. The hypothesis proposed in this work is that data is located exactly or near along with a low dimension manifold and the tool for finding this manifold is auto-encoders. In particular, we calculate the basis of the tangent space of the low-dimensional manifold at each data point and up towards using it to the regularization of the regression task. All calculations are implemented via Python 3 since this programming language in clude...
We claim and present arguments to the effect that a large class of manifold learning algorithms that...
peer reviewedHigh-dimensional data generated by a system with limited degrees of freedom are often c...
International audienceWe claim and present arguments to the effect that a large class of manifold le...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. On...
We are increasingly confronted with very high dimensional data from speech,images, genomes, and othe...
Abstract—This paper proposes a new approach to analyze high-dimensional data set using low-dimension...
Manifold learning is a popular recent approach to nonlinear dimensionality reduction. Algorithms for...
The information explosion of the past few decades has created tremendous opportunities for Machine L...
Modeling data generated by physiological systems is a crucial step in many problems such as classifi...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
dissertationIntrinsic dimension estimation is a fundamental problem in manifold learning. In applica...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we sh...
The problem of dimensionality reduction arises in many fields of information processing, including m...
We study the problem of discovering a manifold that best preserves information relevant to a nonline...
We claim and present arguments to the effect that a large class of manifold learning algorithms that...
peer reviewedHigh-dimensional data generated by a system with limited degrees of freedom are often c...
International audienceWe claim and present arguments to the effect that a large class of manifold le...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. On...
We are increasingly confronted with very high dimensional data from speech,images, genomes, and othe...
Abstract—This paper proposes a new approach to analyze high-dimensional data set using low-dimension...
Manifold learning is a popular recent approach to nonlinear dimensionality reduction. Algorithms for...
The information explosion of the past few decades has created tremendous opportunities for Machine L...
Modeling data generated by physiological systems is a crucial step in many problems such as classifi...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
dissertationIntrinsic dimension estimation is a fundamental problem in manifold learning. In applica...
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we sh...
The problem of dimensionality reduction arises in many fields of information processing, including m...
We study the problem of discovering a manifold that best preserves information relevant to a nonline...
We claim and present arguments to the effect that a large class of manifold learning algorithms that...
peer reviewedHigh-dimensional data generated by a system with limited degrees of freedom are often c...
International audienceWe claim and present arguments to the effect that a large class of manifold le...