The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite number of bits; moreover, ther...
Vershynin. Algorithmic linear dimension reduction in the l1 norm for sparse vectors. 44th Annual Allerton Conference on Communication, Control, and Computing, 2006.A. Gilbert, M. Strauss, J. Tropp, and R. Vershynin. Algorithmic linear dimension reduction in the ℓ1 norm for sparse vectors....
miltiorrhiza were identified according to the sparse loading vectors. The levels of these metabolites were quantified and evaluated by Kruskal–Wallis tests and also showed significant difference. ResultsTwenty-six primary and secondary metabolites were identified in samples from different regions. The ...
Vectors.sparse(size, ret_indices, ret_values).asInstanceOf[SparseVector] } 调用:val w=rand2(4,3) 返回,SparseVector有3个要素函数,size,indices,values. 取某一部分就调用那3个函数。比如:w.size=4,w.indices=(1,2,3,4) def rand3(numRows:Int,numCols:Int,seed:Int):SparseMatrix={ val rand...
Compute the conjugate dot product of vectors (Hilbert space). x · y = xˆH * y. That is, multiply the conjugate complex number of an element in vector x by the corresponding element in vector y and then add the products. The conjugate of the complex number a + bi is a - bi. ...
The energy compaction feature of such sparse coefficient vectors is then evaluated in a lossy hyperspectral data compression framework. Experimental results on a number of hyperspectral data show that this approach is effective in hyperspectral data compression, and comparable to some of the state-of-...
[pk, int64, float64, string float_vector] data file: vectors.npy and uid.npy, Steps: 1. create collection 2. import data 3. verify """ self._connect() fields = [ cf.gen_int64_field(name=df.pk_field, is_primary=True, auto_id=auto_id), cf.gen_int64_field(name=df.int_field)...
Given a dictionary D = {d_k} of vectors d_k, we seek to represent a signal S as a linear combination S = Σ_k γ(k)d_k, with scalar coefficients γ(k). In particular, we aim for the sparsest representation possible. In general, this requires a combinatorial optimization process. ...
对应于mahout中的org.apache.mahout.vectorizer.SparseVectorsFromSequenceFiles,从昨天跑的算法中的任务监控界面可以看到这一步包含了7个Job信息,分别是:(1)DocumentTokenizer(2)WordCount(3)MakePartialVectors(4)MergePartialVectors(5)VectorTfIdf Document Frequency Count(6)MakePartialVectors(7)MergePartialVectors。
Summary: We review three recovery algorithms used in compressive sensing for the reconstruction of $s$-sparse vectors $\\bold x \\in \\Bbb C^{N}$ from the mere knowledge of linear measurements $\\bold y=A\\bold x\\in \\Bbb C^{m}, ~m S Foucart - Springer New York 被引量: 24...