Naoki Saito

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We describe an extension to the \best-basis" method to construct an orthonormal basis which maximizes a class separability for signal classiication problems. This algorithm reduces the dimen-sionality of these problems by using basis functions which are well localized in time-frequency plane as feature extractors. We tested our method using two synthetic(More)
We describe an algorithm to estimate a discrete signal from its noisy observation, using a library of orthonormal bases (consisting of various wavelets, wavelet packets, and local trigonometric bases) and the information-theoretic criterion called minimum description length (MDL). The key to eeective random noise suppression is that the signal component in(More)
We propose a new method to analyze and represent data recorded on a domain of general shape in R d by computing the eigenfunctions of Laplacian defined over there and expanding the data into these eigenfunctions. Instead of directly solving the eigenvalue problem on such a domain via the Helmholtz equation (which can be quite complicated and costly), we(More)
BACKGROUND MazF is an endoribonuclease encoded by Escherichia coli that specifically cleaves the ACA sequence of mRNA. In our previous report, conditional expression of MazF in the HIV-1 LTR rendered CD4+ T lymphocytes resistant to HIV-1 replication. In this study, we examined the in vivo safety and persistence of MazF-transduced cynomolgus macaque CD4+ T(More)
Recently developed classification and regression methods are applied to extract geological information from acoustic well-logging waveforms. First, acoustic waveforms are classified into the ones propagated through sandstones and the ones propagated through shale using the local discriminant basis (LDB) method. Next, the volume fractions of minerals are(More)
We introduce a new local sine transform that can completely localize image information both in the space domain and in the spatial frequency domain. This transform, which we shall call the polyharmonic local sine transform (PHLST), first segments an image into local pieces using the characteristic functions, then decomposes each piece into two components:(More)
We examine the similarity and difference between sparsity and statistical independence in image representations in a very concrete setting: use the best basis algorithm to select the sparsest basis and the least statistically-dependent basis from basis dictionaries for a given dataset. In order to understand their relationship, we use synthetic stochastic(More)