Robust manifold learning with CycleCut

Robust manifold learning with CycleCut

Many manifold learning algorithms utilize graphs of local neighborhoods to estimate manifold topology. When neighborhood connections short-circuit between geodesically distant regions of the manifold, poor results are obtained due to the compromises that the manifold learner must make to satisfy the erroneous criteria. Also, existing manifold learning algorithms have difficulty unfolding manifo...

Robust Multiple Manifold Structure Learning

We present a robust multiple manifolds structure learning (RMMSL) scheme to robustly estimate data structures under the multiple low intrinsic dimensional manifolds assumption. In the local learning stage, RMMSL efficiently estimates local tangent space by weighted low-rank matrix factorization. In the global learning stage, we propose a robust manifold clustering method based on local structur...

Learning the Time-delay Manifold for Robust Speaker Localization

We present an algorithm for high dimensional density estimation which is efficient (both computationally and statistically) when the distribution is concentrated close to a low dimensional smooth manifold. The algorithm uses several random projections to generate a hierarchical mixture of Gaussians which rapidly converges to the underlying manifold. We use this algorithm to perform robust estim...

Robust Manifold Unfolding with Kernel Regularization

Robust cartogram visualization of outliers in manifold learning

Most real data sets contain atypical observations, often referred to as outliers. Their presence may have a negative impact in data modeling using machine learning. This is particularly the case in data density estimation approaches. Manifold learning techniques provide low-dimensional data representations, often oriented towards visualization. The visualization provided by density estimation m...